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 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 80.90 % 78.46 % 84.13 % 84.22 % 84.58 % 87.46 % 90.47 % 87.48 %

Benchmark TP FP FN
CAR 32910 1482 1335

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.70 % 86.07 % 91.81 % 37 78.37 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.54 % 9.54 % 2.92 % 116

Benchmark # Dets # Tracks
CAR 34245 777

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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