Module deep_ocsort 

Deep OC-SORT: Observation-Centric SORT with appearance

This module implements the Deep OC-SORT algorithm.

Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification Gerard Maggiolino, Adnan Ahmad, Jinkun Cao, Kris Kitani arXiv:2302.11813

Algorithm overview

Deep OC-SORT extends OC-SORT by adding an appearance term to the association:

• OCM adds a velocity direction-consistency bonus to the IoU before matching.
• ORU replays interpolated observations to correct the Kalman filter after a track is re-associated following a gap.
• Appearance association blends a cosine distance to each track’s feature gallery with the motion cost. The appearance weight scales with detector confidence (dynamic appearance) and is gated by max_cosine_distance. With appearance_weight = 0 the association reduces to plain OC-SORT.
• Camera motion compensation (CMC) warps track predictions by a caller-supplied affine transform before association, for moving-camera footage. The transform is shared common::cmc infrastructure reused by other trackers.

This is a clean-room implementation. The tracker applies a camera-motion transform but does not estimate it: the caller supplies the affine (for example from image registration), keeping the core free of heavy computer-vision dependencies.

Builds on

• utils::kalman - the shared 8-dimensional Kalman filter
• utils::geometry - iou_batch, tlwh_to_xyah, xyah_to_tlwh
• utils::assignment - greedy_match
• trackers::common - KalmanTrack, TrackState, and CameraMotion (CMC)
• trackers::deepsort - NearestNeighborDistanceMetric for the cosine feature gallery

Parameters

Parameter	Default	Description
max_age	30	Frames a lost track survives before deletion
min_hits	3	Consecutive matches required to confirm a track
iou_threshold	0.3	Minimum IoU to associate a detection with a track
delta_t	3	Observation window (frames) used to compute velocity (OCM)
inertia	0.2	Weight of the direction-consistency cost bonus (OCM)
appearance_weight	0.5	Blend weight of the appearance cost, scaled by det. score
max_cosine_distance	0.2	Maximum cosine distance for the appearance term to apply
nn_budget	100	Maximum appearance features stored per track

Rust API

use trackforge::trackers::deep_ocsort::DeepOcSort;

// `extractor` implements the AppearanceExtractor trait (plug in any Re-ID model).
let mut tracker = DeepOcSort::new(extractor, 30, 3, 0.3, 3, 0.2, 0.5, 0.2, 100);
let tracks = tracker.update(&image, detections)?;
for t in tracks {
    println!("ID: {}, Box: {:?}", t.track_id, t.tlwh);
}

Python API

from trackforge import DEEPOCSORT

tracker = DEEPOCSORT(
    max_age=30,
    min_hits=3,
    iou_threshold=0.3,
    delta_t=3,
    inertia=0.2,
    appearance_weight=0.5,
    max_cosine_distance=0.2,
    nn_budget=100,
)

detections = [([100.0, 100.0, 50.0, 100.0], 0.9, 0)]
embeddings = [[0.1, 0.2, 0.3]]  # one appearance vector per detection
tracks = tracker.update(detections, embeddings)

# Moving camera: pass a [a, b, tx, c, d, ty] affine mapping the previous frame
# to the current one (estimate it however you like, e.g. with OpenCV).
camera_motion = [1.0, 0.0, 12.0, 0.0, 1.0, -4.0]
tracks = tracker.update(detections, embeddings, camera_motion)

for track_id, tlwh, score, class_id, det_ind in tracks:
    print(f"ID: {track_id}, Box: {tlwh}")

Credit

Clean-room Rust implementation of the algorithm described in the paper above. Original reference implementation: GerardMaggiolino/Deep-OC-SORT.

Citation

@inproceedings{maggiolino2023deepocsort,
  title={Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification},
  author={Maggiolino, Gerard and Ahmad, Adnan and Cao, Jinkun and Kitani, Kris},
  booktitle={IEEE International Conference on Image Processing (ICIP)},
  year={2023}
}

Re-exports

pub use crate::trackers::common::ObsTrack as DeepOcSortTrack;

Structs

DeepOcSortParams

Settings for DeepOcSort.

DeepOcSortTracker

Deep OC-SORT tracker core.