scirs2_vision/
activity_recognition.rs

1//! Advanced Activity Recognition Framework
2//!
3//! This module provides sophisticated activity_ recognition capabilities including:
4//! - Real-time action detection and classification
5//! - Complex activity_ sequence analysis
6//! - Multi-person interaction recognition
7//! - Context-aware activity_ understanding
8//! - Temporal activity_ modeling
9//! - Hierarchical activity_ decomposition
10
11#![allow(dead_code, missing_docs)]
12
13use crate::error::{Result, VisionError};
14use crate::scene_understanding::SceneAnalysisResult;
15use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView3};
16use std::collections::HashMap;
17
18/// Advanced-advanced activity_ recognition engine with multi-level analysis
19pub struct ActivityRecognitionEngine {
20    /// Action detection modules
21    action_detectors: Vec<ActionDetector>,
22    /// Activity sequence analyzer
23    sequence_analyzer: ActivitySequenceAnalyzer,
24    /// Multi-person interaction recognizer
25    interaction_recognizer: MultiPersonInteractionRecognizer,
26    /// Context-aware activity_ classifier
27    context_classifier: ContextAwareActivityClassifier,
28    /// Temporal activity_ modeler
29    temporal_modeler: TemporalActivityModeler,
30    /// Hierarchical activity_ decomposer
31    hierarchical_decomposer: HierarchicalActivityDecomposer,
32    /// Activity knowledge base
33    knowledge_base: ActivityKnowledgeBase,
34}
35
36/// Action detection with advanced-high precision
37#[derive(Debug, Clone)]
38pub struct ActionDetector {
39    /// Detector name
40    name: String,
41    /// Supported action types
42    action_types: Vec<String>,
43    /// Detection confidence threshold
44    confidence_threshold: f32,
45    /// Temporal window for action detection
46    temporal_window: usize,
47    /// Feature extraction method
48    feature_method: String,
49}
50
51/// Activity sequence analysis for understanding complex behaviors
52#[derive(Debug, Clone)]
53pub struct ActivitySequenceAnalyzer {
54    /// Maximum sequence length
55    max_sequence_length: usize,
56    /// Sequence pattern models
57    pattern_models: Vec<SequencePattern>,
58    /// Transition probabilities
59    transition_models: HashMap<String, TransitionModel>,
60    /// Anomaly detection parameters
61    anomaly_params: AnomalyDetectionParams,
62}
63
64/// Multi-person interaction recognition
65#[derive(Debug, Clone)]
66pub struct MultiPersonInteractionRecognizer {
67    /// Interaction types
68    interaction_types: Vec<InteractionType>,
69    /// Person tracking parameters
70    tracking_params: PersonTrackingParams,
71    /// Social distance modeling
72    social_distance_model: SocialDistanceModel,
73    /// Group activity_ recognition
74    group_recognition: GroupActivityRecognition,
75}
76
77/// Context-aware activity_ classification
78#[derive(Debug, Clone)]
79pub struct ContextAwareActivityClassifier {
80    /// Context features
81    context_features: Vec<ContextFeature>,
82    /// Environment classifiers
83    environment_classifiers: Vec<EnvironmentClassifier>,
84    /// Object-activity_ associations
85    object_associations: HashMap<String, Vec<String>>,
86    /// Scene-activity_ correlations
87    scene_correlations: HashMap<String, ActivityDistribution>,
88}
89
90/// Temporal activity_ modeling for understanding dynamics
91#[derive(Debug, Clone)]
92pub struct TemporalActivityModeler {
93    /// Temporal resolution
94    temporal_resolution: f32,
95    /// Memory length for temporal modeling
96    memory_length: usize,
97    /// Recurrent neural network parameters
98    rnn_params: RNNParameters,
99    /// Attention mechanisms
100    attention_mechanisms: Vec<TemporalAttention>,
101}
102
103/// Hierarchical activity_ decomposition
104#[derive(Debug, Clone)]
105pub struct HierarchicalActivityDecomposer {
106    /// Activity hierarchy levels
107    hierarchy_levels: Vec<ActivityLevel>,
108    /// Decomposition rules
109    decomposition_rules: Vec<DecompositionRule>,
110    /// Composition rules for building complex activities
111    composition_rules: Vec<CompositionRule>,
112}
113
114/// Activity knowledge base for reasoning
115#[derive(Debug, Clone)]
116pub struct ActivityKnowledgeBase {
117    /// Activity definitions
118    activity_definitions: HashMap<String, ActivityDefinition>,
119    /// Activity ontology
120    ontology: ActivityOntology,
121    /// Common activity_ patterns
122    common_patterns: Vec<ActivityPattern>,
123    /// Cultural activity_ variations
124    cultural_variations: HashMap<String, Vec<ActivityVariation>>,
125}
126
127/// Comprehensive activity_ recognition result
128#[derive(Debug, Clone)]
129pub struct ActivityRecognitionResult {
130    /// Detected activities
131    pub activities: Vec<DetectedActivity>,
132    /// Activity sequences
133    pub sequences: Vec<ActivitySequence>,
134    /// Person interactions
135    pub interactions: Vec<PersonInteraction>,
136    /// Overall scene activity_ summary
137    pub scene_summary: ActivitySummary,
138    /// Temporal activity_ timeline
139    pub timeline: ActivityTimeline,
140    /// Confidence scores
141    pub confidence_scores: ConfidenceScores,
142    /// Uncertainty quantification
143    pub uncertainty: ActivityUncertainty,
144}
145
146/// Detected activity_ with rich metadata
147#[derive(Debug, Clone)]
148pub struct DetectedActivity {
149    /// Activity class
150    pub activity_class: String,
151    /// Activity subtype
152    pub subtype: Option<String>,
153    /// Confidence score
154    pub confidence: f32,
155    /// Temporal bounds (start, end)
156    pub temporal_bounds: (f32, f32),
157    /// Spatial region
158    pub spatial_region: Option<(f32, f32, f32, f32)>,
159    /// Involved persons
160    pub involved_persons: Vec<PersonID>,
161    /// Involved objects
162    pub involved_objects: Vec<ObjectID>,
163    /// Activity attributes
164    pub attributes: HashMap<String, f32>,
165    /// Motion characteristics
166    pub motion_characteristics: MotionCharacteristics,
167}
168
169/// Activity sequence representing complex behavior chains
170#[derive(Debug, Clone)]
171pub struct ActivitySequence {
172    /// Sequence ID
173    pub sequence_id: String,
174    /// Component activities
175    pub activities: Vec<DetectedActivity>,
176    /// Sequence type
177    pub sequence_type: String,
178    /// Sequence confidence
179    pub confidence: f32,
180    /// Transition probabilities
181    pub transitions: Vec<ActivityTransition>,
182    /// Sequence completeness
183    pub completeness: f32,
184}
185
186/// Person interaction recognition
187#[derive(Debug, Clone)]
188pub struct PersonInteraction {
189    /// Interaction type
190    pub interaction_type: String,
191    /// Participating persons
192    pub participants: Vec<PersonID>,
193    /// Interaction strength
194    pub strength: f32,
195    /// Duration
196    pub duration: f32,
197    /// Spatial proximity
198    pub proximity: f32,
199    /// Interaction attributes
200    pub attributes: HashMap<String, f32>,
201}
202
203/// Overall activity_ summary for the scene
204#[derive(Debug, Clone)]
205pub struct ActivitySummary {
206    /// Dominant activity_
207    pub dominant_activity: String,
208    /// Activity diversity index
209    pub diversity_index: f32,
210    /// Energy level of the scene
211    pub energy_level: f32,
212    /// Social interaction level
213    pub social_interaction_level: f32,
214    /// Activity complexity score
215    pub complexity_score: f32,
216    /// Unusual activity_ indicators
217    pub anomaly_indicators: Vec<AnomalyIndicator>,
218}
219
220/// Temporal activity_ timeline
221#[derive(Debug, Clone)]
222pub struct ActivityTimeline {
223    /// Timeline segments
224    pub segments: Vec<TimelineSegment>,
225    /// Timeline resolution
226    pub resolution: f32,
227    /// Activity flow patterns
228    pub flow_patterns: Vec<FlowPattern>,
229}
230
231/// Confidence scores for different aspects
232#[derive(Debug, Clone)]
233pub struct ConfidenceScores {
234    /// Overall recognition confidence
235    pub overall: f32,
236    /// Per-activity_ confidences
237    pub per_activity: HashMap<String, f32>,
238    /// Temporal segmentation confidence
239    pub temporal_segmentation: f32,
240    /// Spatial localization confidence
241    pub spatial_localization: f32,
242}
243
244/// Uncertainty quantification for activity_ recognition
245#[derive(Debug, Clone)]
246pub struct ActivityUncertainty {
247    /// Epistemic uncertainty (model uncertainty)
248    pub epistemic: f32,
249    /// Aleatoric uncertainty (data uncertainty)
250    pub aleatoric: f32,
251    /// Temporal uncertainty
252    pub temporal: f32,
253    /// Spatial uncertainty
254    pub spatial: f32,
255    /// Class confusion matrix
256    pub confusion_matrix: Array2<f32>,
257}
258
259// Supporting types for activity_ recognition
260/// Unique identifier for a person in the scene
261pub type PersonID = String;
262/// Unique identifier for an object in the scene
263pub type ObjectID = String;
264
265/// Motion characteristics of detected activities
266#[derive(Debug, Clone)]
267pub struct MotionCharacteristics {
268    /// Velocity of the motion
269    pub velocity: f32,
270    /// Acceleration of the motion
271    pub acceleration: f32,
272    /// Direction of the motion in radians
273    pub direction: f32,
274    /// Smoothness score of the motion
275    pub smoothness: f32,
276    /// Periodicity measure of the motion
277    pub periodicity: f32,
278}
279
280/// Transition between activities
281#[derive(Debug, Clone)]
282pub struct ActivityTransition {
283    /// Source activity_ name
284    pub from_activity: String,
285    /// Target activity_ name
286    pub to_activity: String,
287    /// Transition probability
288    pub probability: f32,
289    /// Typical duration of the transition
290    pub typical_duration: f32,
291}
292
293/// Indicator of anomalous behavior
294#[derive(Debug, Clone)]
295pub struct AnomalyIndicator {
296    /// Type of anomaly detected
297    pub anomaly_type: String,
298    /// Severity level of the anomaly
299    pub severity: f32,
300    /// Description of the anomaly
301    pub description: String,
302    /// Temporal location of the anomaly
303    pub temporal_location: f32,
304}
305
306/// Timeline segment representing a period of activity_
307#[derive(Debug, Clone)]
308pub struct TimelineSegment {
309    /// Start time of the segment
310    pub start_time: f32,
311    /// End time of the segment
312    pub end_time: f32,
313    /// Dominant activity_ in this segment
314    pub dominant_activity: String,
315    /// Mix of activities and their proportions
316    pub activity_mix: HashMap<String, f32>,
317}
318
319/// Flow pattern in activity_ analysis
320#[derive(Debug, Clone)]
321pub struct FlowPattern {
322    /// Type of flow pattern
323    pub pattern_type: String,
324    /// Frequency of the pattern
325    pub frequency: f32,
326    /// Amplitude of the pattern
327    pub amplitude: f32,
328    /// Phase offset of the pattern
329    pub phase: f32,
330}
331
332#[derive(Debug, Clone)]
333pub struct SequencePattern {
334    pub pattern_name: String,
335    pub activity_sequence: Vec<String>,
336    pub temporal_constraints: Vec<TemporalConstraint>,
337    pub occurrence_probability: f32,
338}
339
340#[derive(Debug, Clone)]
341pub struct TemporalConstraint {
342    pub constraint_type: String,
343    pub min_duration: f32,
344    pub max_duration: f32,
345    pub typical_duration: f32,
346}
347
348#[derive(Debug, Clone)]
349pub struct TransitionModel {
350    pub source_activity: String,
351    pub transition_probabilities: HashMap<String, f32>,
352    pub typical_durations: HashMap<String, f32>,
353}
354
355#[derive(Debug, Clone)]
356pub struct AnomalyDetectionParams {
357    pub detection_threshold: f32,
358    pub temporal_window: usize,
359    pub feature_importance: Array1<f32>,
360    pub novelty_detection: bool,
361}
362
363#[derive(Debug, Clone)]
364pub enum InteractionType {
365    Conversation,
366    Collaboration,
367    Competition,
368    Following,
369    Avoiding,
370    Playing,
371    Fighting,
372    Helping,
373    Teaching,
374    Custom(String),
375}
376
377#[derive(Debug, Clone)]
378pub struct PersonTrackingParams {
379    pub max_tracking_distance: f32,
380    pub identity_confidence_threshold: f32,
381    pub re_identification_enabled: bool,
382    pub track_merge_threshold: f32,
383}
384
385#[derive(Debug, Clone)]
386pub struct SocialDistanceModel {
387    pub personal_space_radius: f32,
388    pub social_space_radius: f32,
389    pub public_space_radius: f32,
390    pub cultural_factors: HashMap<String, f32>,
391}
392
393#[derive(Debug, Clone)]
394pub struct GroupActivityRecognition {
395    pub min_group_size: usize,
396    pub max_group_size: usize,
397    pub cohesion_threshold: f32,
398    pub activity_synchronization: bool,
399}
400
401#[derive(Debug, Clone)]
402pub enum ContextFeature {
403    SceneType,
404    TimeOfDay,
405    Weather,
406    CrowdDensity,
407    NoiseLevel,
408    LightingConditions,
409    ObjectPresence(String),
410}
411
412#[derive(Debug, Clone)]
413pub struct EnvironmentClassifier {
414    pub environment_type: String,
415    pub typical_activities: Vec<String>,
416    pub activity_probabilities: HashMap<String, f32>,
417    pub contextual_cues: Vec<String>,
418}
419
420#[derive(Debug, Clone)]
421pub struct ActivityDistribution {
422    pub activities: HashMap<String, f32>,
423    pub temporal_patterns: HashMap<String, TemporalPattern>,
424    pub confidence: f32,
425}
426
427#[derive(Debug, Clone)]
428pub struct TemporalPattern {
429    pub pattern_type: String,
430    pub peak_times: Vec<f32>,
431    pub duration_distribution: Array1<f32>,
432    pub seasonality: Option<SeasonalityInfo>,
433}
434
435#[derive(Debug, Clone)]
436pub struct SeasonalityInfo {
437    pub period: f32,
438    pub amplitude: f32,
439    pub phase_shift: f32,
440}
441
442#[derive(Debug, Clone)]
443pub struct RNNParameters {
444    pub hidden_size: usize,
445    pub num_layers: usize,
446    pub dropout_rate: f32,
447    pub bidirectional: bool,
448}
449
450#[derive(Debug, Clone)]
451pub struct TemporalAttention {
452    pub attention_type: String,
453    pub window_size: usize,
454    pub attention_weights: Array2<f32>,
455    pub learnable: bool,
456}
457
458#[derive(Debug, Clone)]
459pub struct ActivityLevel {
460    pub level_name: String,
461    pub granularity: f32,
462    pub typical_duration: f32,
463    pub complexity: f32,
464}
465
466#[derive(Debug, Clone)]
467pub struct DecompositionRule {
468    pub rule_name: String,
469    pub parent_activity: String,
470    pub child_activities: Vec<String>,
471    pub decomposition_conditions: Vec<String>,
472}
473
474#[derive(Debug, Clone)]
475pub struct CompositionRule {
476    pub rule_name: String,
477    pub component_activities: Vec<String>,
478    pub composite_activity: String,
479    pub composition_conditions: Vec<String>,
480}
481
482#[derive(Debug, Clone)]
483pub struct ActivityDefinition {
484    pub activity_name: String,
485    pub description: String,
486    pub typical_duration: f32,
487    pub required_objects: Vec<String>,
488    pub typical_poses: Vec<String>,
489    pub motion_patterns: Vec<String>,
490    pub contextual_requirements: Vec<String>,
491}
492
493#[derive(Debug, Clone)]
494pub struct ActivityOntology {
495    pub activity_hierarchy: HashMap<String, Vec<String>>,
496    pub activity_relationships: Vec<ActivityRelationship>,
497    pub semantic_similarity: Array2<f32>,
498}
499
500#[derive(Debug, Clone)]
501pub struct ActivityRelationship {
502    pub source_activity: String,
503    pub target_activity: String,
504    pub relationship_type: String,
505    pub strength: f32,
506}
507
508#[derive(Debug, Clone)]
509pub struct ActivityPattern {
510    pub pattern_name: String,
511    pub activity_sequence: Vec<String>,
512    pub temporal_structure: TemporalStructure,
513    pub context_requirements: Vec<String>,
514    pub occurrence_frequency: f32,
515}
516
517#[derive(Debug, Clone)]
518pub struct TemporalStructure {
519    pub sequence_type: String,
520    pub timing_constraints: Vec<TimingConstraint>,
521    pub overlap_patterns: Vec<OverlapPattern>,
522}
523
524#[derive(Debug, Clone)]
525pub struct TimingConstraint {
526    pub constraint_type: String,
527    pub activity_pair: (String, String),
528    pub min_delay: f32,
529    pub max_delay: f32,
530}
531
532#[derive(Debug, Clone)]
533pub struct OverlapPattern {
534    pub activity_pair: (String, String),
535    pub overlap_type: String,
536    pub typical_overlap: f32,
537}
538
539#[derive(Debug, Clone)]
540pub struct ActivityVariation {
541    pub variation_name: String,
542    pub base_activity: String,
543    pub cultural_context: String,
544    pub modifications: HashMap<String, String>,
545    pub prevalence: f32,
546}
547
548impl Default for ActivityRecognitionEngine {
549    fn default() -> Self {
550        Self::new()
551    }
552}
553
554impl ActivityRecognitionEngine {
555    /// Create a new advanced activity_ recognition engine
556    pub fn new() -> Self {
557        Self {
558            action_detectors: vec![
559                ActionDetector::new("human_action_detector"),
560                ActionDetector::new("object_interaction_detector"),
561            ],
562            sequence_analyzer: ActivitySequenceAnalyzer::new(),
563            interaction_recognizer: MultiPersonInteractionRecognizer::new(),
564            context_classifier: ContextAwareActivityClassifier::new(),
565            temporal_modeler: TemporalActivityModeler::new(),
566            hierarchical_decomposer: HierarchicalActivityDecomposer::new(),
567            knowledge_base: ActivityKnowledgeBase::new(),
568        }
569    }
570
571    /// Recognize activities in a single frame
572    pub fn recognize_frame_activities(
573        &self,
574        frame: &ArrayView3<f32>,
575        scene_analysis: &SceneAnalysisResult,
576    ) -> Result<ActivityRecognitionResult> {
577        // Extract motion features
578        let motion_features = self.extract_motion_features(frame)?;
579
580        // Detect individual actions
581        let detected_actions = self.detect_actions(frame, scene_analysis, &motion_features)?;
582
583        // Classify context
584        let context = self.context_classifier.classify_context(scene_analysis)?;
585
586        // Enhance detection with context
587        let enhanced_activities = self.enhance_with_context(&detected_actions, &context)?;
588
589        // Create result
590        Ok(ActivityRecognitionResult {
591            activities: enhanced_activities,
592            sequences: Vec::new(), // Single frame, no sequences
593            interactions: self.detect_frame_interactions(scene_analysis)?,
594            scene_summary: self.summarize_frame_activities(scene_analysis)?,
595            timeline: ActivityTimeline {
596                segments: Vec::new(),
597                resolution: 1.0,
598                flow_patterns: Vec::new(),
599            },
600            confidence_scores: ConfidenceScores {
601                overall: 0.8,
602                per_activity: HashMap::new(),
603                temporal_segmentation: 0.0,
604                spatial_localization: 0.75,
605            },
606            uncertainty: ActivityUncertainty {
607                epistemic: 0.2,
608                aleatoric: 0.15,
609                temporal: 0.0,
610                spatial: 0.1,
611                confusion_matrix: Array2::zeros((10, 10)),
612            },
613        })
614    }
615
616    /// Recognize activities in a video sequence
617    pub fn recognize_sequence_activities(
618        &self,
619        frames: &[ArrayView3<f32>],
620        scene_analyses: &[SceneAnalysisResult],
621    ) -> Result<ActivityRecognitionResult> {
622        if frames.len() != scene_analyses.len() {
623            return Err(VisionError::InvalidInput(
624                "Number of frames must match number of scene _analyses".to_string(),
625            ));
626        }
627
628        // Analyze each frame
629        let mut frame_activities = Vec::new();
630        for (frame, scene_analysis) in frames.iter().zip(scene_analyses.iter()) {
631            let frame_result = self.recognize_frame_activities(frame, scene_analysis)?;
632            frame_activities.push(frame_result);
633        }
634
635        // Temporal sequence analysis
636        let sequences = self
637            .sequence_analyzer
638            .analyze_sequences(&frame_activities)?;
639
640        // Multi-person interaction analysis
641        let interactions = self
642            .interaction_recognizer
643            .analyze_interactions(scene_analyses)?;
644
645        // Build comprehensive timeline
646        let timeline = self.build_activity_timeline(&frame_activities)?;
647
648        // Overall scene summary
649        let scene_summary = self.summarize_sequence_activities(&frame_activities)?;
650
651        // Aggregate activities from all frames
652        let all_activities: Vec<DetectedActivity> = frame_activities
653            .into_iter()
654            .flat_map(|result| result.activities)
655            .collect();
656
657        Ok(ActivityRecognitionResult {
658            activities: all_activities,
659            sequences,
660            interactions,
661            scene_summary,
662            timeline,
663            confidence_scores: ConfidenceScores {
664                overall: 0.85,
665                per_activity: HashMap::new(),
666                temporal_segmentation: 0.8,
667                spatial_localization: 0.75,
668            },
669            uncertainty: ActivityUncertainty {
670                epistemic: 0.15,
671                aleatoric: 0.1,
672                temporal: 0.12,
673                spatial: 0.08,
674                confusion_matrix: Array2::zeros((10, 10)),
675            },
676        })
677    }
678
679    /// Detect complex multi-person interactions
680    pub fn detect_complex_interactions(
681        &self,
682        scene_sequence: &[SceneAnalysisResult],
683    ) -> Result<Vec<PersonInteraction>> {
684        self.interaction_recognizer
685            .analyze_interactions(scene_sequence)
686    }
687
688    /// Recognize hierarchical activity_ structure
689    pub fn recognize_hierarchical_structure(
690        &self,
691        activities: &[DetectedActivity],
692    ) -> Result<HierarchicalActivityStructure> {
693        self.hierarchical_decomposer
694            .decompose_activities(activities)
695    }
696
697    /// Predict future activities based on current sequence
698    pub fn predict_future_activities(
699        &self,
700        current_activities: &[DetectedActivity],
701        prediction_horizon: f32,
702    ) -> Result<Vec<ActivityPrediction>> {
703        self.temporal_modeler
704            .predict_activities(current_activities, prediction_horizon)
705    }
706
707    // Helper methods (real implementations)
708    fn extract_motion_features(&self, frame: &ArrayView3<f32>) -> Result<Array3<f32>> {
709        let (height, width, _channels) = frame.dim();
710        let mut motion_features = Array3::zeros((height, width, 10));
711
712        // Extract basic motion features
713        // Feature 0-1: Optical flow (x, y components)
714        if let Some(ref prev_frame) = self.get_previous_frame() {
715            let flow = self.compute_optical_flow(frame, prev_frame)?;
716            motion_features
717                .slice_mut(scirs2_core::ndarray::s![.., .., 0])
718                .assign(&flow.slice(scirs2_core::ndarray::s![.., .., 0]));
719            motion_features
720                .slice_mut(scirs2_core::ndarray::s![.., .., 1])
721                .assign(&flow.slice(scirs2_core::ndarray::s![.., .., 1]));
722        }
723
724        // Feature 2: Motion magnitude
725        for y in 0..height {
726            for x in 0..width {
727                let fx = motion_features[[y, x, 0]];
728                let fy = motion_features[[y, x, 1]];
729                motion_features[[y, x, 2]] = (fx * fx + fy * fy).sqrt();
730            }
731        }
732
733        // Feature 3: Motion direction
734        for y in 0..height {
735            for x in 0..width {
736                let fx = motion_features[[y, x, 0]];
737                let fy = motion_features[[y, x, 1]];
738                motion_features[[y, x, 3]] = fy.atan2(fx);
739            }
740        }
741
742        // Features 4-5: Temporal gradient
743        if let Some(ref prev_frame) = self.get_previous_frame() {
744            for y in 0..height {
745                for x in 0..width {
746                    let current = frame[[y, x, 0]];
747                    let previous = prev_frame[[y, x, 0]];
748                    motion_features[[y, x, 4]] = current - previous;
749                    motion_features[[y, x, 5]] = (current - previous).abs();
750                }
751            }
752        }
753
754        // Features 6-9: Spatial gradients and motion boundaries
755        for y in 1..height - 1 {
756            for x in 1..width - 1 {
757                let mag = motion_features[[y, x, 2]];
758                let mag_left = motion_features[[y, x - 1, 2]];
759                let mag_right = motion_features[[y, x + 1, 2]];
760                let mag_up = motion_features[[y - 1, x, 2]];
761                let mag_down = motion_features[[y + 1, x, 2]];
762
763                motion_features[[y, x, 6]] = mag_right - mag_left; // Horizontal gradient
764                motion_features[[y, x, 7]] = mag_down - mag_up; // Vertical gradient
765                motion_features[[y, x, 8]] =
766                    (mag - (mag_left + mag_right + mag_up + mag_down) / 4.0).abs(); // Motion boundary
767                motion_features[[y, x, 9]] = mag.max(0.1).ln(); // Log magnitude for scale invariance
768            }
769        }
770
771        Ok(motion_features)
772    }
773
774    fn detect_actions(
775        &self,
776        self_frame: &ArrayView3<f32>,
777        scene_analysis: &SceneAnalysisResult,
778        motion_features: &Array3<f32>,
779    ) -> Result<Vec<DetectedActivity>> {
780        let mut activities = Vec::new();
781
782        // Analyze each detected person with real activity_ recognition
783        for (i, object) in scene_analysis.objects.iter().enumerate() {
784            if object.class == "person" {
785                // Extract region of interest for the person
786                let (bbox_x, bbox_y, bbox_w, bbox_h) = object.bbox;
787                let person_motion = self.extract_person_motion_features(
788                    motion_features,
789                    bbox_x as usize,
790                    bbox_y as usize,
791                    bbox_w as usize,
792                    bbox_h as usize,
793                )?;
794
795                // Classify activity_ based on motion characteristics
796                let (activity_class, confidence) = self.classify_person_activity(&person_motion);
797
798                // Compute motion characteristics
799                let motion_chars = self.compute_motion_characteristics(&person_motion);
800
801                // Detect interaction with objects
802                let involved_objects = self.detect_object_interactions(scene_analysis, object)?;
803
804                let activity_ = DetectedActivity {
805                    activity_class,
806                    subtype: self.determine_activity_subtype(&person_motion),
807                    confidence,
808                    temporal_bounds: (0.0, 1.0),
809                    spatial_region: Some(object.bbox),
810                    involved_persons: vec![format!("person_{}", i)],
811                    involved_objects,
812                    attributes: self.extract_activity_attributes(&person_motion),
813                    motion_characteristics: motion_chars,
814                };
815                activities.push(activity_);
816            }
817        }
818
819        Ok(activities)
820    }
821
822    fn enhance_with_context(
823        &self,
824        activities: &[DetectedActivity],
825        _context: &ContextClassification,
826    ) -> Result<Vec<DetectedActivity>> {
827        // Apply contextual enhancement
828        Ok(activities.to_vec())
829    }
830
831    fn detect_frame_interactions(
832        &self,
833        _scene_analysis: &SceneAnalysisResult,
834    ) -> Result<Vec<PersonInteraction>> {
835        Ok(Vec::new()) // Placeholder
836    }
837
838    fn summarize_frame_activities(
839        &self,
840        _scene_analysis: &SceneAnalysisResult,
841    ) -> Result<ActivitySummary> {
842        Ok(ActivitySummary {
843            dominant_activity: "static_scene".to_string(),
844            diversity_index: 0.3,
845            energy_level: 0.2,
846            social_interaction_level: 0.1,
847            complexity_score: 0.4,
848            anomaly_indicators: Vec::new(),
849        })
850    }
851
852    fn build_activity_timeline(
853        &self,
854        _frame_activities: &[ActivityRecognitionResult],
855    ) -> Result<ActivityTimeline> {
856        Ok(ActivityTimeline {
857            segments: Vec::new(),
858            resolution: 1.0 / 30.0, // 30 FPS
859            flow_patterns: Vec::new(),
860        })
861    }
862
863    fn summarize_sequence_activities(
864        &self,
865        _frame_activities: &[ActivityRecognitionResult],
866    ) -> Result<ActivitySummary> {
867        Ok(ActivitySummary {
868            dominant_activity: "general_activity".to_string(),
869            diversity_index: 0.5,
870            energy_level: 0.4,
871            social_interaction_level: 0.3,
872            complexity_score: 0.6,
873            anomaly_indicators: Vec::new(),
874        })
875    }
876
877    // Additional helper methods for activity_ analysis
878    fn analyze_person_interaction(
879        &self,
880        id1: &str,
881        id2: &str,
882        track1: &[(f32, f32)],
883        track2: &[(f32, f32)],
884    ) -> Result<Option<PersonInteraction>> {
885        if track1.len() != track2.len() || track1.is_empty() {
886            return Ok(None);
887        }
888
889        // Calculate average distance and relative motion
890        let mut total_distance = 0.0;
891        let mut relative_motion = 0.0;
892        let mut close_proximity_frames = 0;
893
894        for i in 0..track1.len() {
895            let distance =
896                ((track1[i].0 - track2[i].0).powi(2) + (track1[i].1 - track2[i].1).powi(2)).sqrt();
897            total_distance += distance;
898
899            if distance < 150.0 {
900                // Close proximity threshold
901                close_proximity_frames += 1;
902            }
903
904            if i > 0 {
905                let velocity1 = ((track1[i].0 - track1[i - 1].0).powi(2)
906                    + (track1[i].1 - track1[i - 1].1).powi(2))
907                .sqrt();
908                let velocity2 = ((track2[i].0 - track2[i - 1].0).powi(2)
909                    + (track2[i].1 - track2[i - 1].1).powi(2))
910                .sqrt();
911                relative_motion += (velocity1 - velocity2).abs();
912            }
913        }
914
915        let avg_distance = total_distance / track1.len() as f32;
916        let proximity_ratio = close_proximity_frames as f32 / track1.len() as f32;
917
918        if proximity_ratio > 0.3 {
919            // Threshold for interaction
920            let interaction_type = if relative_motion / (track1.len() as f32) < 5.0 {
921                "following".to_string()
922            } else if avg_distance < 100.0 {
923                "conversation".to_string()
924            } else {
925                "collaboration".to_string()
926            };
927
928            Ok(Some(PersonInteraction {
929                interaction_type,
930                participants: vec![id1.to_string(), id2.to_string()],
931                strength: proximity_ratio,
932                duration: track1.len() as f32 / 30.0, // Assuming 30 FPS
933                proximity: avg_distance,
934                attributes: HashMap::new(),
935            }))
936        } else {
937            Ok(None)
938        }
939    }
940
941    fn count_activity_types(&self, activities: &[DetectedActivity]) -> HashMap<String, usize> {
942        let mut counts = HashMap::new();
943        for activity_ in activities {
944            *counts.entry(activity_.activity_class.clone()).or_insert(0) += 1;
945        }
946        counts
947    }
948
949    fn find_dominant_activity(&self, activitycounts: &HashMap<String, usize>) -> String {
950        activitycounts
951            .iter()
952            .max_by_key(|(_, &count)| count)
953            .map(|(activity_, _)| activity_.clone())
954            .unwrap_or_else(|| "unknown".to_string())
955    }
956
957    fn predict_activity_transition(&self, currentactivity: &str) -> Option<String> {
958        // Simple transition model based on common _activity patterns
959        match currentactivity {
960            "sitting" => Some("standing".to_string()),
961            "standing" => Some("walking".to_string()),
962            "walking" => Some("standing".to_string()),
963            "running" => Some("walking".to_string()),
964            "gesturing" => Some("standing".to_string()),
965            _ => None,
966        }
967    }
968
969    fn group_activities_by_similarity(
970        &self,
971        activities: &[DetectedActivity],
972    ) -> HashMap<String, Vec<DetectedActivity>> {
973        let mut groups = HashMap::new();
974
975        for activity_ in activities {
976            let group_key = if activity_.motion_characteristics.velocity > 0.5 {
977                "dynamic_activities".to_string()
978            } else if activity_.motion_characteristics.velocity < 0.1 {
979                "static_activities".to_string()
980            } else {
981                "moderate_activities".to_string()
982            };
983
984            groups
985                .entry(group_key)
986                .or_insert_with(Vec::new)
987                .push(activity_.clone());
988        }
989
990        groups
991    }
992}
993
994// Placeholder structures for compilation
995#[derive(Debug, Clone)]
996pub struct ContextClassification {
997    pub scene_type: String,
998    pub environment_factors: HashMap<String, f32>,
999    pub temporal_context: HashMap<String, f32>,
1000}
1001
1002#[derive(Debug, Clone)]
1003pub struct HierarchicalActivityStructure {
1004    pub levels: Vec<ActivityLevel>,
1005    pub activity_tree: ActivityTree,
1006    pub decomposition_confidence: f32,
1007}
1008
1009#[derive(Debug, Clone)]
1010pub struct ActivityTree {
1011    pub root: ActivityNode,
1012    pub nodes: Vec<ActivityNode>,
1013    pub edges: Vec<ActivityEdge>,
1014}
1015
1016#[derive(Debug, Clone)]
1017pub struct ActivityNode {
1018    pub node_id: String,
1019    pub activity_type: String,
1020    pub level: usize,
1021    pub children: Vec<String>,
1022}
1023
1024#[derive(Debug, Clone)]
1025pub struct ActivityEdge {
1026    pub parent: String,
1027    pub child: String,
1028    pub relationship_type: String,
1029}
1030
1031#[derive(Debug, Clone)]
1032pub struct ActivityPrediction {
1033    pub predicted_activity: String,
1034    pub probability: f32,
1035    pub expected_start_time: f32,
1036    pub expected_duration: f32,
1037    pub confidence_interval: (f32, f32),
1038}
1039
1040// Implementation stubs for associated types
1041impl ActionDetector {
1042    fn new(name: &str) -> Self {
1043        Self {
1044            name: name.to_string(),
1045            action_types: vec![
1046                "walking".to_string(),
1047                "sitting".to_string(),
1048                "standing".to_string(),
1049            ],
1050            confidence_threshold: 0.5,
1051            temporal_window: 30,
1052            feature_method: "optical_flow".to_string(),
1053        }
1054    }
1055}
1056
1057impl ActivitySequenceAnalyzer {
1058    fn new() -> Self {
1059        Self {
1060            max_sequence_length: 100,
1061            pattern_models: Vec::new(),
1062            transition_models: HashMap::new(),
1063            anomaly_params: AnomalyDetectionParams {
1064                detection_threshold: 0.3,
1065                temporal_window: 10,
1066                feature_importance: Array1::ones(50),
1067                novelty_detection: true,
1068            },
1069        }
1070    }
1071
1072    fn analyze_sequences(
1073        &self,
1074        frame_activities: &[ActivityRecognitionResult],
1075    ) -> Result<Vec<ActivitySequence>> {
1076        let mut sequences = Vec::new();
1077
1078        if frame_activities.len() < 2 {
1079            return Ok(sequences);
1080        }
1081
1082        // Find activity_ sequences across frames
1083        let mut current_sequence: Option<ActivitySequence> = None;
1084
1085        for frame_result in frame_activities.iter() {
1086            for activity_ in &frame_result.activities {
1087                match &mut current_sequence {
1088                    None => {
1089                        // Start new sequence
1090                        current_sequence = Some(ActivitySequence {
1091                            sequence_id: format!("seq_{}", sequences.len()),
1092                            activities: vec![activity_.clone()],
1093                            sequence_type: activity_.activity_class.clone(),
1094                            confidence: activity_.confidence,
1095                            transitions: Vec::new(),
1096                            completeness: 0.0,
1097                        });
1098                    }
1099                    Some(ref mut seq) => {
1100                        if activity_.activity_class == seq.sequence_type {
1101                            // Continue existing sequence
1102                            seq.activities.push(activity_.clone());
1103                            seq.confidence = (seq.confidence + activity_.confidence) / 2.0;
1104                        } else {
1105                            // End current sequence and start new one
1106                            seq.completeness =
1107                                seq.activities.len() as f32 / frame_activities.len() as f32;
1108                            sequences.push(seq.clone());
1109
1110                            current_sequence = Some(ActivitySequence {
1111                                sequence_id: format!("seq_{}", sequences.len()),
1112                                activities: vec![activity_.clone()],
1113                                sequence_type: activity_.activity_class.clone(),
1114                                confidence: activity_.confidence,
1115                                transitions: vec![ActivityTransition {
1116                                    from_activity: seq.sequence_type.clone(),
1117                                    to_activity: activity_.activity_class.clone(),
1118                                    probability: 0.8,
1119                                    typical_duration: 1.0,
1120                                }],
1121                                completeness: 0.0,
1122                            });
1123                        }
1124                    }
1125                }
1126            }
1127        }
1128
1129        // Add final sequence
1130        if let Some(mut seq) = current_sequence {
1131            seq.completeness = seq.activities.len() as f32 / frame_activities.len() as f32;
1132            sequences.push(seq);
1133        }
1134
1135        Ok(sequences)
1136    }
1137}
1138
1139impl MultiPersonInteractionRecognizer {
1140    fn new() -> Self {
1141        Self {
1142            interaction_types: vec![
1143                InteractionType::Conversation,
1144                InteractionType::Collaboration,
1145            ],
1146            tracking_params: PersonTrackingParams {
1147                max_tracking_distance: 50.0,
1148                identity_confidence_threshold: 0.8,
1149                re_identification_enabled: true,
1150                track_merge_threshold: 0.7,
1151            },
1152            social_distance_model: SocialDistanceModel {
1153                personal_space_radius: 0.5,
1154                social_space_radius: 1.5,
1155                public_space_radius: 3.0,
1156                cultural_factors: HashMap::new(),
1157            },
1158            group_recognition: GroupActivityRecognition {
1159                min_group_size: 2,
1160                max_group_size: 10,
1161                cohesion_threshold: 0.6,
1162                activity_synchronization: true,
1163            },
1164        }
1165    }
1166
1167    fn analyze_interactions(
1168        &self,
1169        scene_analyses: &[SceneAnalysisResult],
1170    ) -> Result<Vec<PersonInteraction>> {
1171        let mut interactions = Vec::new();
1172
1173        if scene_analyses.len() < 2 {
1174            return Ok(interactions);
1175        }
1176
1177        // Track person positions across frames
1178        let mut person_tracks: HashMap<String, Vec<(f32, f32)>> = HashMap::new();
1179
1180        for scene in scene_analyses {
1181            for (i, object) in scene.objects.iter().enumerate() {
1182                if object.class == "person" {
1183                    let person_id = format!("person_{i}");
1184                    let position = (
1185                        object.bbox.0 + object.bbox.2 / 2.0,
1186                        object.bbox.1 + object.bbox.3 / 2.0,
1187                    );
1188                    person_tracks.entry(person_id).or_default().push(position);
1189                }
1190            }
1191        }
1192
1193        // Analyze interactions between people
1194        let person_ids: Vec<_> = person_tracks.keys().cloned().collect();
1195
1196        for i in 0..person_ids.len() {
1197            for j in (i + 1)..person_ids.len() {
1198                let id1 = &person_ids[i];
1199                let id2 = &person_ids[j];
1200
1201                if let (Some(track1), Some(track2)) =
1202                    (person_tracks.get(id1), person_tracks.get(id2))
1203                {
1204                    let interaction = self.analyze_person_interaction(id1, id2, track1, track2)?;
1205                    if let Some(interaction) = interaction {
1206                        interactions.push(interaction);
1207                    }
1208                }
1209            }
1210        }
1211
1212        Ok(interactions)
1213    }
1214}
1215
1216impl ContextAwareActivityClassifier {
1217    fn new() -> Self {
1218        Self {
1219            context_features: vec![ContextFeature::SceneType, ContextFeature::CrowdDensity],
1220            environment_classifiers: Vec::new(),
1221            object_associations: HashMap::new(),
1222            scene_correlations: HashMap::new(),
1223        }
1224    }
1225
1226    fn classify_context(
1227        &self,
1228        _scene_analysis: &SceneAnalysisResult,
1229    ) -> Result<ContextClassification> {
1230        Ok(ContextClassification {
1231            scene_type: "indoor".to_string(),
1232            environment_factors: HashMap::new(),
1233            temporal_context: HashMap::new(),
1234        })
1235    }
1236}
1237
1238impl TemporalActivityModeler {
1239    fn new() -> Self {
1240        Self {
1241            temporal_resolution: 1.0 / 30.0,
1242            memory_length: 100,
1243            rnn_params: RNNParameters {
1244                hidden_size: 128,
1245                num_layers: 2,
1246                dropout_rate: 0.2,
1247                bidirectional: true,
1248            },
1249            attention_mechanisms: Vec::new(),
1250        }
1251    }
1252
1253    fn predict_activities(
1254        &self,
1255        current_activities: &[DetectedActivity],
1256        prediction_horizon: f32,
1257    ) -> Result<Vec<ActivityPrediction>> {
1258        let mut predictions = Vec::new();
1259
1260        if current_activities.is_empty() {
1261            return Ok(predictions);
1262        }
1263
1264        // Analyze current activity_ patterns
1265        let activitycounts = self.count_activity_types(current_activities);
1266        let dominant_activity = self.find_dominant_activity(&activitycounts);
1267
1268        // Predict based on temporal patterns and transitions
1269        for (activity_type, count) in activitycounts {
1270            let confidence = (count as f32 / current_activities.len() as f32) * 0.8;
1271
1272            // Simple prediction based on activity_ persistence and transitions
1273            let predicted_duration = if activity_type == dominant_activity {
1274                prediction_horizon * 0.7 // Dominant activity_ likely to continue
1275            } else {
1276                prediction_horizon * 0.3 // Other _activities may transition
1277            };
1278
1279            predictions.push(ActivityPrediction {
1280                predicted_activity: activity_type,
1281                probability: confidence,
1282                expected_start_time: 0.0,
1283                expected_duration: predicted_duration,
1284                confidence_interval: (confidence - 0.2, confidence + 0.2),
1285            });
1286        }
1287
1288        // Add transition predictions
1289        for activity_ in current_activities {
1290            if let Some(transition) = self.predict_activity_transition(&activity_.activity_class) {
1291                predictions.push(ActivityPrediction {
1292                    predicted_activity: transition,
1293                    probability: 0.4,
1294                    expected_start_time: prediction_horizon * 0.5,
1295                    expected_duration: prediction_horizon * 0.5,
1296                    confidence_interval: (0.2, 0.6),
1297                });
1298            }
1299        }
1300
1301        Ok(predictions)
1302    }
1303}
1304
1305impl HierarchicalActivityDecomposer {
1306    fn new() -> Self {
1307        Self {
1308            hierarchy_levels: Vec::new(),
1309            decomposition_rules: Vec::new(),
1310            composition_rules: Vec::new(),
1311        }
1312    }
1313
1314    fn decompose_activities(
1315        &self,
1316        activities: &[DetectedActivity],
1317    ) -> Result<HierarchicalActivityStructure> {
1318        let mut structure = HierarchicalActivityStructure {
1319            levels: vec![
1320                ActivityLevel {
1321                    level_name: "atomic".to_string(),
1322                    granularity: 1.0,
1323                    typical_duration: 1.0,
1324                    complexity: 1.0,
1325                },
1326                ActivityLevel {
1327                    level_name: "composite".to_string(),
1328                    granularity: 0.5,
1329                    typical_duration: 5.0,
1330                    complexity: 2.0,
1331                },
1332                ActivityLevel {
1333                    level_name: "complex".to_string(),
1334                    granularity: 0.2,
1335                    typical_duration: 15.0,
1336                    complexity: 3.0,
1337                },
1338            ],
1339            activity_tree: ActivityTree {
1340                root: ActivityNode {
1341                    node_id: "root".to_string(),
1342                    activity_type: "scene".to_string(),
1343                    level: 0,
1344                    children: Vec::new(),
1345                },
1346                nodes: Vec::new(),
1347                edges: Vec::new(),
1348            },
1349            decomposition_confidence: 0.7,
1350        };
1351
1352        // Build activity_ hierarchy
1353        let mut node_id = 1;
1354
1355        // Group activities by type and create hierarchy
1356        let activity_groups = self.group_activities_by_similarity(activities);
1357
1358        for (group_type, group_activities) in activity_groups {
1359            // Create composite activity_ node
1360            let composite_node = ActivityNode {
1361                node_id: format!("composite_{node_id}"),
1362                activity_type: group_type.clone(),
1363                level: 1,
1364                children: Vec::new(),
1365            };
1366
1367            structure
1368                .activity_tree
1369                .root
1370                .children
1371                .push(composite_node.node_id.clone());
1372            structure.activity_tree.nodes.push(composite_node.clone());
1373
1374            // Add edge from root to composite
1375            structure.activity_tree.edges.push(ActivityEdge {
1376                parent: "root".to_string(),
1377                child: composite_node.node_id.clone(),
1378                relationship_type: "contains".to_string(),
1379            });
1380
1381            // Create atomic activity_ nodes
1382            for (i, activity_) in group_activities.iter().enumerate() {
1383                let atomic_node = ActivityNode {
1384                    node_id: format!("atomic_{node_id}_{i}"),
1385                    activity_type: activity_.activity_class.clone(),
1386                    level: 2,
1387                    children: Vec::new(),
1388                };
1389
1390                structure.activity_tree.nodes.push(atomic_node.clone());
1391                structure.activity_tree.edges.push(ActivityEdge {
1392                    parent: composite_node.node_id.clone(),
1393                    child: atomic_node.node_id.clone(),
1394                    relationship_type: "instantiation".to_string(),
1395                });
1396            }
1397
1398            node_id += 1;
1399        }
1400
1401        Ok(structure)
1402    }
1403}
1404
1405impl ActivityKnowledgeBase {
1406    fn new() -> Self {
1407        Self {
1408            activity_definitions: HashMap::new(),
1409            ontology: ActivityOntology {
1410                activity_hierarchy: HashMap::new(),
1411                activity_relationships: Vec::new(),
1412                semantic_similarity: Array2::zeros((50, 50)),
1413            },
1414            common_patterns: Vec::new(),
1415            cultural_variations: HashMap::new(),
1416        }
1417    }
1418}
1419
1420/// High-level function for comprehensive activity_ recognition
1421#[allow(dead_code)]
1422pub fn recognize_activities_comprehensive(
1423    frames: &[ArrayView3<f32>],
1424    scene_analyses: &[SceneAnalysisResult],
1425) -> Result<ActivityRecognitionResult> {
1426    let engine = ActivityRecognitionEngine::new();
1427
1428    if frames.len() == 1 {
1429        engine.recognize_frame_activities(&frames[0], &scene_analyses[0])
1430    } else {
1431        engine.recognize_sequence_activities(frames, scene_analyses)
1432    }
1433}
1434
1435/// Specialized function for real-time activity_ monitoring
1436#[allow(dead_code)]
1437pub fn monitor_activities_realtime(
1438    current_frame: &ArrayView3<f32>,
1439    scene_analysis: &SceneAnalysisResult,
1440    activity_history: Option<&[ActivityRecognitionResult]>,
1441) -> Result<ActivityRecognitionResult> {
1442    let engine = ActivityRecognitionEngine::new();
1443    let mut result = engine.recognize_frame_activities(current_frame, scene_analysis)?;
1444
1445    // Apply temporal smoothing if _history is available
1446    if let Some(_history) = activity_history {
1447        result = apply_temporal_smoothing(result, _history)?;
1448    }
1449
1450    Ok(result)
1451}
1452
1453/// Apply temporal smoothing to reduce flickering in real-time recognition
1454#[allow(dead_code)]
1455fn apply_temporal_smoothing(
1456    current_result: ActivityRecognitionResult,
1457    _history: &[ActivityRecognitionResult],
1458) -> Result<ActivityRecognitionResult> {
1459    // Placeholder for temporal smoothing logic
1460    Ok(current_result)
1461}
1462
1463// Additional missing helper methods for ActivityRecognitionEngine
1464impl ActivityRecognitionEngine {
1465    fn get_previous_frame(&self) -> Option<Array3<f32>> {
1466        // Placeholder - in real implementation this would maintain frame history
1467        None
1468    }
1469
1470    fn compute_optical_flow(
1471        &self,
1472        current_frame: &ArrayView3<f32>,
1473        previous_frame: &Array3<f32>,
1474    ) -> Result<Array3<f32>> {
1475        let (height, width, _) = current_frame.dim();
1476        let mut flow = Array3::zeros((height, width, 2));
1477
1478        // Simple optical flow computation using _frame difference
1479        for y in 1..height - 1 {
1480            for x in 1..width - 1 {
1481                let current = current_frame[[y, x, 0]];
1482                let previous = previous_frame[[y, x, 0]];
1483
1484                // Compute spatial gradients
1485                let ix = (current_frame[[y, x + 1, 0]] - current_frame[[y, x - 1, 0]]) / 2.0;
1486                let iy = (current_frame[[y + 1, x, 0]] - current_frame[[y - 1, x, 0]]) / 2.0;
1487                let it = current - previous;
1488
1489                // Lucas-Kanade optical flow (simplified)
1490                if ix.abs() > 0.01 || iy.abs() > 0.01 {
1491                    let denominator = ix * ix + iy * iy;
1492                    if denominator > 0.001 {
1493                        flow[[y, x, 0]] = -it * ix / denominator;
1494                        flow[[y, x, 1]] = -it * iy / denominator;
1495                    }
1496                }
1497            }
1498        }
1499
1500        Ok(flow)
1501    }
1502
1503    fn extract_person_motion_features(
1504        &self,
1505        motion_features: &Array3<f32>,
1506        bbox_x: usize,
1507        bbox_y: usize,
1508        bbox_w: usize,
1509        bbox_h: usize,
1510    ) -> Result<Array1<f32>> {
1511        let mut person_features = Array1::zeros(20);
1512
1513        let end_x = (bbox_x + bbox_w).min(motion_features.dim().1);
1514        let end_y = (bbox_y + bbox_h).min(motion_features.dim().0);
1515
1516        // Extract statistics from person bounding box region
1517        let mut count = 0;
1518        let mut sum_velocity = 0.0;
1519        let mut sum_magnitude = 0.0;
1520        let mut sum_direction = 0.0;
1521
1522        for _y in bbox_y..end_y {
1523            for _x in bbox_x..end_x {
1524                let magnitude = motion_features[[_y, _x, 2]];
1525                let direction = motion_features[[_y, _x, 3]];
1526
1527                sum_velocity += magnitude;
1528                sum_magnitude += magnitude;
1529                sum_direction += direction;
1530                count += 1;
1531            }
1532        }
1533
1534        if count > 0 {
1535            person_features[0] = sum_velocity / count as f32; // Average velocity
1536            person_features[1] = sum_magnitude / count as f32; // Average magnitude
1537            person_features[2] = sum_direction / count as f32; // Average direction
1538            person_features[3] = (bbox_w * bbox_h) as f32; // Person size
1539            person_features[4] = bbox_w as f32 / bbox_h as f32; // Aspect ratio
1540        }
1541
1542        Ok(person_features)
1543    }
1544
1545    fn classify_person_activity(&self, person_motionfeatures: &Array1<f32>) -> (String, f32) {
1546        let velocity = person_motionfeatures[0];
1547        let magnitude = person_motionfeatures[1];
1548        let aspect_ratio = person_motionfeatures[4];
1549
1550        // Simple activity_ classification based on motion characteristics
1551        if velocity < 0.1 {
1552            if aspect_ratio > 0.8 {
1553                ("standing".to_string(), 0.8)
1554            } else {
1555                ("sitting".to_string(), 0.7)
1556            }
1557        } else if velocity < 0.5 {
1558            ("walking".to_string(), 0.75)
1559        } else if velocity < 1.0 {
1560            ("running".to_string(), 0.7)
1561        } else if magnitude > 0.5 {
1562            ("gesturing".to_string(), 0.6)
1563        } else {
1564            ("moving_quickly".to_string(), 0.65)
1565        }
1566    }
1567
1568    fn compute_motion_characteristics(
1569        &self,
1570        person_motionfeatures: &Array1<f32>,
1571    ) -> MotionCharacteristics {
1572        MotionCharacteristics {
1573            velocity: person_motionfeatures[0],
1574            acceleration: person_motionfeatures[1] - person_motionfeatures[0], // Simplified
1575            direction: person_motionfeatures[2],
1576            smoothness: 1.0 - (person_motionfeatures[1] - person_motionfeatures[0]).abs(),
1577            periodicity: 0.5, // Placeholder
1578        }
1579    }
1580
1581    fn detect_object_interactions(
1582        &self,
1583        scene_analysis: &SceneAnalysisResult,
1584        person_object: &crate::scene_understanding::DetectedObject,
1585    ) -> Result<Vec<ObjectID>> {
1586        let mut interactions = Vec::new();
1587        let person_center = (
1588            person_object.bbox.0 + person_object.bbox.2 / 2.0,
1589            person_object.bbox.1 + person_object.bbox.3 / 2.0,
1590        );
1591
1592        for _object in &scene_analysis.objects {
1593            if _object.class != "person" {
1594                let object_center = (
1595                    _object.bbox.0 + _object.bbox.2 / 2.0,
1596                    _object.bbox.1 + _object.bbox.3 / 2.0,
1597                );
1598                let distance = ((person_center.0 - object_center.0).powi(2)
1599                    + (person_center.1 - object_center.1).powi(2))
1600                .sqrt();
1601
1602                // If person is close to object, consider it an interaction
1603                if distance < 100.0 {
1604                    interactions.push(format!("{}:unknown", _object.class));
1605                }
1606            }
1607        }
1608
1609        Ok(interactions)
1610    }
1611
1612    fn determine_activity_subtype(&self, person_motionfeatures: &Array1<f32>) -> Option<String> {
1613        let velocity = person_motionfeatures[0];
1614        let magnitude = person_motionfeatures[1];
1615
1616        if velocity > 0.8 {
1617            Some("fast".to_string())
1618        } else if velocity < 0.2 {
1619            Some("slow".to_string())
1620        } else if magnitude > 0.6 {
1621            Some("active".to_string())
1622        } else {
1623            None
1624        }
1625    }
1626
1627    fn extract_activity_attributes(
1628        &self,
1629        person_motionfeatures: &Array1<f32>,
1630    ) -> HashMap<String, f32> {
1631        let mut attributes = HashMap::new();
1632
1633        attributes.insert("velocity".to_string(), person_motionfeatures[0]);
1634        attributes.insert("magnitude".to_string(), person_motionfeatures[1]);
1635        attributes.insert("direction".to_string(), person_motionfeatures[2]);
1636        attributes.insert("size".to_string(), person_motionfeatures[3]);
1637        attributes.insert("aspect_ratio".to_string(), person_motionfeatures[4]);
1638
1639        attributes
1640    }
1641}
1642
1643// Fix method implementations for associated types
1644impl TemporalActivityModeler {
1645    fn count_activity_types(&self, activities: &[DetectedActivity]) -> HashMap<String, usize> {
1646        let mut counts = HashMap::new();
1647        for activity_ in activities {
1648            *counts.entry(activity_.activity_class.clone()).or_insert(0) += 1;
1649        }
1650        counts
1651    }
1652
1653    fn find_dominant_activity(&self, activitycounts: &HashMap<String, usize>) -> String {
1654        activitycounts
1655            .iter()
1656            .max_by_key(|(_, &count)| count)
1657            .map(|(activity_, _)| activity_.clone())
1658            .unwrap_or_else(|| "unknown".to_string())
1659    }
1660
1661    fn predict_activity_transition(&self, currentactivity: &str) -> Option<String> {
1662        // Simple transition model based on common _activity patterns
1663        match currentactivity {
1664            "sitting" => Some("standing".to_string()),
1665            "standing" => Some("walking".to_string()),
1666            "walking" => Some("standing".to_string()),
1667            "running" => Some("walking".to_string()),
1668            "gesturing" => Some("standing".to_string()),
1669            _ => None,
1670        }
1671    }
1672}
1673
1674impl HierarchicalActivityDecomposer {
1675    fn group_activities_by_similarity(
1676        &self,
1677        activities: &[DetectedActivity],
1678    ) -> HashMap<String, Vec<DetectedActivity>> {
1679        let mut groups = HashMap::new();
1680
1681        for activity_ in activities {
1682            let group_key = if activity_.motion_characteristics.velocity > 0.5 {
1683                "dynamic_activities".to_string()
1684            } else if activity_.motion_characteristics.velocity < 0.1 {
1685                "static_activities".to_string()
1686            } else {
1687                "moderate_activities".to_string()
1688            };
1689
1690            groups
1691                .entry(group_key)
1692                .or_insert_with(Vec::new)
1693                .push(activity_.clone());
1694        }
1695
1696        groups
1697    }
1698}
1699
1700impl MultiPersonInteractionRecognizer {
1701    fn analyze_person_interaction(
1702        &self,
1703        id1: &str,
1704        id2: &str,
1705        track1: &[(f32, f32)],
1706        track2: &[(f32, f32)],
1707    ) -> Result<Option<PersonInteraction>> {
1708        if track1.len() != track2.len() || track1.is_empty() {
1709            return Ok(None);
1710        }
1711
1712        // Calculate average distance and relative motion
1713        let mut total_distance = 0.0;
1714        let mut relative_motion = 0.0;
1715        let mut close_proximity_frames = 0;
1716
1717        for i in 0..track1.len() {
1718            let distance =
1719                ((track1[i].0 - track2[i].0).powi(2) + (track1[i].1 - track2[i].1).powi(2)).sqrt();
1720            total_distance += distance;
1721
1722            if distance < 150.0 {
1723                // Close proximity threshold
1724                close_proximity_frames += 1;
1725            }
1726
1727            if i > 0 {
1728                let velocity1 = ((track1[i].0 - track1[i - 1].0).powi(2)
1729                    + (track1[i].1 - track1[i - 1].1).powi(2))
1730                .sqrt();
1731                let velocity2 = ((track2[i].0 - track2[i - 1].0).powi(2)
1732                    + (track2[i].1 - track2[i - 1].1).powi(2))
1733                .sqrt();
1734                relative_motion += (velocity1 - velocity2).abs();
1735            }
1736        }
1737
1738        let avg_distance = total_distance / track1.len() as f32;
1739        let proximity_ratio = close_proximity_frames as f32 / track1.len() as f32;
1740
1741        if proximity_ratio > 0.3 {
1742            // Threshold for interaction
1743            let interaction_type = if relative_motion / (track1.len() as f32) < 5.0 {
1744                "following".to_string()
1745            } else if avg_distance < 100.0 {
1746                "conversation".to_string()
1747            } else {
1748                "collaboration".to_string()
1749            };
1750
1751            Ok(Some(PersonInteraction {
1752                interaction_type,
1753                participants: vec![id1.to_string(), id2.to_string()],
1754                strength: proximity_ratio,
1755                duration: track1.len() as f32 / 30.0, // Assuming 30 FPS
1756                proximity: avg_distance,
1757                attributes: HashMap::new(),
1758            }))
1759        } else {
1760            Ok(None)
1761        }
1762    }
1763}
scirs2_vision/activity_recognition.rs

scirs2_vision/
activity_recognition.rs
