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 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 71.46 % 76.76 % 67.12 % 81.16 % 87.00 % 71.44 % 85.84 % 88.08 %
PEDESTRIAN 57.63 % 63.66 % 53.12 % 67.59 % 77.78 % 58.96 % 73.36 % 80.89 %

Benchmark TP FP FN
CAR 33413 3347 880
PEDESTRIAN 17356 3341 631

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 86.83 % 86.83 % 88.50 % 616 74.85 %
PEDESTRIAN 78.92 % 78.16 % 80.81 % 390 60.61 %

Benchmark MT rate PT rate ML rate FRAG
CAR 80.18 % 18.32 % 1.50 % 727
PEDESTRIAN 60.37 % 35.19 % 4.44 % 661

Benchmark # Dets # Tracks
CAR 34293 893
PEDESTRIAN 17987 403

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.


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