Method

SearchTrack [SearchTrack]
https://github.com/qa276390/SearchTrack

Submitted on 22 Dec. 2021 08:03 by
ZhongMin Tsai ( Communication and Multimedia Laboratory, National Taiwan University)

Running time:0.19 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
A Search-Based Tracker with Position-Aware Motion
Model
Parameters:
none
Latex Bibtex:
@inproceedings{tsai2022searchtrack,
title={SearchTrack: Multiple Object Tracking with
Object-Customized Search and Motion-Aware
Features},
author={Tsai, Zhong-Min and Tsai, Yu-Ju and Wang,
Chien-Yao and Liao, Hong-Yuan and Lin, Youn-Long and
Chuang, Yung-Yu},
booktitle={BMVC},
year={2022}
}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics (adapted for the segmentation case): CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark sMOTSA MOTSA MOTSP MODSA MODSP
CAR 74.80 % 86.80 % 86.80 % 88.50 % 89.70 %
PEDESTRIAN 60.60 % 78.90 % 78.20 % 80.80 % 93.00 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.90 % 97.40 % 94.00 % 33407 886 3353 8.00 % 45994 1299
PEDESTRIAN 83.80 % 96.50 % 89.70 % 17350 637 3347 5.70 % 25145 728

Benchmark MT PT ML IDS FRAG
CAR 80.00 % 18.50 % 1.50 % 614 983
PEDESTRIAN 60.40 % 35.20 % 4.40 % 390 714

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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