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 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 87.13 % 85.17 % 87.19 % 88.04 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.07 % 98.34 % 94.02 % 34673 584 3823 5.25 % 39374 768

Benchmark MT PT ML IDS FRAG
CAR 75.85 % 14.77 % 9.38 % 19 533

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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