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 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 78.00 % 90.40 % 87.20 % 91.80 % 89.70 %
PEDESTRIAN 61.00 % 75.70 % 81.30 % 76.90 % 93.80 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 96.10 % 95.80 % 95.90 % 35317 1558 1443 14.00 % 52899 1174
PEDESTRIAN 78.70 % 97.80 % 87.20 % 16280 371 4417 3.30 % 19981 708

Benchmark MT PT ML IDS FRAG
CAR 91.30 % 8.00 % 0.80 % 542 832
PEDESTRIAN 53.00 % 38.50 % 8.50 % 233 707

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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