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 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
PEDESTRIAN 54.44 % 62.29 % 48.08 % 65.49 % 81.17 % 64.97 % 58.66 % 83.28 %

Benchmark TP FP FN
PEDESTRIAN 16356 4341 344

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
PEDESTRIAN 76.51 % 80.96 % 77.36 % 176 61.47 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 48.89 % 41.85 % 9.26 % 716

Benchmark # Dets # Tracks
PEDESTRIAN 16700 212

This table as LaTeX