Method

PointTrack with our segmentation results [PointTrack]


Submitted on 28 Feb. 2020 09:57 by
Yanjie Ke (University of Science and Technology of China)

Running time:0.045 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{xu2020Segment,
title={Segment as Points for Efficient Online
Multi-Object Tracking and Segmentation},
author={Xu, Zhenbo and Zhang, Wei and Tan, Xiao
and Yang, Wei and Huang, Huan and Wen, Shilei and
Ding, Errui and Huang, Liusheng},
booktitle={Proceedings of the European
Conference on Computer Vision (ECCV)},
year={2020}
}

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 61.95 % 79.38 % 48.83 % 85.77 % 85.66 % 79.07 % 56.35 % 88.52 %

Benchmark TP FP FN
CAR 35279 1481 1526

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 90.88 % 87.10 % 91.82 % 346 78.50 %

Benchmark MT rate PT rate ML rate FRAG
CAR 90.84 % 8.56 % 0.60 % 538

Benchmark # Dets # Tracks
CAR 36805 385

This table as LaTeX

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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