Method

An Object Point Set Inductive Tracker for Multi-Object Tracking and Segmentation [OPITrack]
https://ieeexplore.ieee.org/document/9881968

Submitted on 20 Sep. 2022 04:27 by
Yan Gao (xidian university)

Running time:0.09 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Y. Gao, H. Xu, Y. Zheng, J. Li and X. Gao, "An
Object Point Set Inductive Tracker for Multi-Object
Tracking and Segmentation," in IEEE Transactions on
Image Processing, 2022, doi:
10.1109/TIP.2022.3203607.
Parameters:
Cars: Ntrain=168, Ntest=1500
Pedestrians: Ntrain=300,Ntest=1500
Latex Bibtex:
@ARTICLE{9881968, author={Gao, Yan and Xu, Haojun
and Zheng, Yu and Li, Jie and Gao, Xinbo},
journal={IEEE Transactions on Image Processing},
title={An Object Point Set Inductive Tracker for
Multi-Object Tracking and Segmentation}, year=
{2022}, volume={}, number={}, pages={1-1},
doi={10.1109/TIP.2022.3203607}}

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 73.04 % 79.44 % 67.97 % 85.90 % 85.64 % 75.66 % 80.02 % 88.57 %
PEDESTRIAN 60.38 % 62.45 % 60.05 % 65.65 % 81.61 % 64.33 % 82.23 % 83.55 %

Benchmark TP FP FN
CAR 35318 1442 1557
PEDESTRIAN 16283 4414 368

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 90.37 % 87.15 % 91.84 % 542 78.02 %
PEDESTRIAN 75.77 % 81.29 % 76.89 % 234 61.05 %

Benchmark MT rate PT rate ML rate FRAG
CAR 91.29 % 7.96 % 0.75 % 525
PEDESTRIAN 52.96 % 38.52 % 8.52 % 719

Benchmark # Dets # Tracks
CAR 36875 879
PEDESTRIAN 16651 438

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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