Method

3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association[la] [PC3T]
https://github.com/hailanyi/3D-Multi-Object-Tracker

Submitted on 24 Dec. 2020 05:37 by
hai wu (xiamen university)

Running time:0.0045 s
Environment:1 core @ >3.5 Ghz (Python + C/C++)

Method Description:
A CA motion model-based 3D multi-object tracker.
Parameters:
TBD
Latex Bibtex:
@article{wu20213d,
title={3D Multi-Object Tracking in Point Clouds
Based on Prediction Confidence-Guided Data
Association},
author={Wu, Hai and Han, Wenkai and Wen, Chenglu
and Li, Xin and Wang, Cheng},
journal={IEEE TITS},
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 77.80 % 74.57 % 81.59 % 79.19 % 84.07 % 84.77 % 88.75 % 86.07 %

Benchmark TP FP FN
CAR 31582 2810 814

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 88.81 % 84.26 % 89.46 % 225 74.35 %

Benchmark MT rate PT rate ML rate FRAG
CAR 80.00 % 11.54 % 8.46 % 201

Benchmark # Dets # Tracks
CAR 32396 866

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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