Method

PointTrack with our segmentation results on PEDESTRIAN [PointTrack]


Submitted on 29 Feb. 2020 03:41 by
Yanjie Ke (University of Science and Technology of China)

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

Method Description:
Segment as Points for Efficient Online Multi-
Object Tracking and Segmentation
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 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 0.00 % 0.00 % 0.00 % 0.00 % 0.00 %
PEDESTRIAN 61.50 % 76.50 % 81.00 % 77.40 % 93.80 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 0.00 % 0.00 % 0.00 % 0 0 0 0.00 % 0 0
PEDESTRIAN 79.00 % 97.90 % 87.50 % 16356 344 4341 3.10 % 20047 243

Benchmark MT PT ML IDS FRAG
CAR 0.00 % 0.00 % 0.00 % 0 0
PEDESTRIAN 48.90 % 41.90 % 9.30 % 176 632

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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