edgefirst-tracker

Multi-object tracking for edge AI applications.

This crate provides object tracking algorithms for associating detections across video frames, enabling applications like counting, path analysis, and re-identification.

Algorithms

Features

• UUID-based track IDs - Globally unique identifiers for each tracked object
• Kalman filtering - Smooth trajectory prediction and state estimation
• Configurable association - IoU-based matching with tunable thresholds
• Track lifecycle - Automatic creation, update, and deletion of tracks

Quick Start

use edgefirst_tracker::{Tracker, TrackInfo, DetectionBox};
use edgefirst_tracker::bytetrack::ByteTracker;

// Create tracker
let mut tracker = ByteTracker::new(
    30,    // max lost frames before track deletion
    0.5,   // high detection threshold
    0.1,   // low detection threshold
    0.8,   // match threshold
);

// Process detections each frame
let detections = get_detections_from_model();
let timestamp = frame_number as u64;

let tracks = tracker.update(&detections, timestamp);

// Each detection gets associated track info (or None if untracked)
for (det, track) in detections.iter().zip(tracks.iter()) {
    if let Some(info) = track {
        println!("Detection matched to track {}", info.uuid);
        println!("  Track age: {} frames", info.count);
        println!("  Predicted location: {:?}", info.tracked_location);
    }
}

// Get all currently active tracks
for track in tracker.get_active_tracks() {
    println!("Active track: {} at {:?}", track.uuid, track.tracked_location);
}

Track Information

Each TrackInfo contains:

Integration

Works with any detection source implementing DetectionBox:

use edgefirst_tracker::DetectionBox;

impl DetectionBox for MyDetection {
    fn bbox(&self) -> [f32; 4] { self.coordinates }
    fn score(&self) -> f32 { self.confidence }
    fn label(&self) -> usize { self.class_id }
}

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.