Method

Cascade TWiX [on] [C-TWiX]
https://github.com/Guepardow/TWiX/

Submitted on 16 Nov. 2024 16:18 by
Mehdi Miah (Polytechnique Montréal)

Running time:0.01 s
Environment:8 cores @ >3.5 Ghz (Python)

Method Description:
No CMC, no visual appearances, no ReID, no BEV, no LIDAR, no depth estimation; only spatio-temporal coordinates (xmin, ymin, xmax, ymax, t).

Detections : Permatrack
Pipeline : Online Cascade matching (cf C-BIoU)

We focused on the association step by training a Transformer-based network to predict the similarity matrix between past tracks and current detections.
Parameters:
Maximal temporal gap at STA/LTA : 0.0s/0.8s
Past window size : 0.4s
Future window size : 1/fps sec
Matching association thresholds at STA/LTA : 0.4/-0.6

Tracking maximum age : 0.8s
Tracking Minimal score : 50%
Latex Bibtex:
@article{miah2024learningdata,
title = {Learning data association for multi-object tracking using only coordinates},
journal = {Pattern Recognition},
volume = {160},
pages = {111169},
year = {2025},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2024.111169},
url = {https://www.sciencedirect.com/science/article/pii/S0031320324009208},
author = {Mehdi Miah and Guillaume-Alexandre Bilodeau and Nicolas Saunier},
keywords = {Tracking, Transformer, Data association, Motion, Multi-object tracking}
}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 77.58 % 76.97 % 78.84 % 80.25 % 86.43 % 81.90 % 88.35 % 86.95 %
PEDESTRIAN 52.44 % 50.83 % 54.35 % 55.26 % 72.37 % 59.45 % 72.89 % 79.15 %

Benchmark TP FP FN
CAR 31578 2814 355
PEDESTRIAN 16518 6632 1160

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.68 % 85.50 % 90.79 % 381 76.36 %
PEDESTRIAN 64.95 % 75.28 % 66.34 % 322 47.31 %

Benchmark MT rate PT rate ML rate FRAG
CAR 81.39 % 15.54 % 3.08 % 335
PEDESTRIAN 42.61 % 39.17 % 18.21 % 684

Benchmark # Dets # Tracks
CAR 31933 773
PEDESTRIAN 17678 380

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


eXTReMe Tracker