Method

PC-TCNN[la] [PC-TCNN]


Submitted on 4 Jan. 2021 02:19 by
hai wu (xiamen university)

Running time:0.3 s
Environment:GPU (python/c++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{wu2021,
title={Tracklet Proposal Network for Multi-Object
Tracking on Point Clouds},
author={Wu, Hai and Li, Qing and Wen, Chenglu and
Li, Xin and Fan, Xiaoliang and Wang, Cheng},
booktitle={IJCAI},
year={2021}
}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 91.75 % 86.17 % 91.82 % 88.52 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 96.08 % 96.45 % 96.26 % 36234 1333 1480 11.98 % 43807 971

Benchmark MT PT ML IDS FRAG
CAR 87.54 % 9.54 % 2.92 % 26 118

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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