Method

[st] [on] Stereo3DMOT: Stereo-based 3D Multi-Object Tracking with Re-identification [Stereo3DMOT]


Submitted on 10 Jun. 2023 17:52 by
Chen Mao (University of Chinese Academy of Sciences)

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
we propose a 3D multi-object tracking system based on
stereo cameras.
Parameters:
alpha=0.8
Latex Bibtex:

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 77.32 % 73.43 % 81.86 % 77.34 % 85.38 % 84.66 % 89.61 % 86.72 %

Benchmark TP FP FN
CAR 30568 3824 585

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 87.10 % 85.06 % 87.18 % 28 73.82 %

Benchmark MT rate PT rate ML rate FRAG
CAR 75.69 % 14.92 % 9.38 % 662

Benchmark # Dets # Tracks
CAR 31153 681

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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