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 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 78.50 % 90.90 % 87.10 % 91.80 % 89.70 %
PEDESTRIAN 0.00 % 0.00 % 0.00 % 0.00 % 0.00 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 96.00 % 95.90 % 95.90 % 35279 1526 1481 13.80 % 52530 415
PEDESTRIAN 0.00 % 0.00 % 0.00 % 0 0 0 0.00 % 0 0

Benchmark MT PT ML IDS FRAG
CAR 90.80 % 8.60 % 0.60 % 346 645
PEDESTRIAN 0.00 % 0.00 % 0.00 % 0 0

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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