Method

PB-MOT: Pose-aware Association Boosted Online 3D Multi-Object Tracking [on] [PB-MOT]


Submitted on 1 Mar. 2025 15:13 by
Bo Pang (Zhejiang University)

Running time:4e-4 s
Environment:>8 cores @ 3.0 Ghz (Python)

Method Description:
N/A
Parameters:
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Latex Bibtex:
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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 91.43 % 86.80 % 91.50 % 89.44 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 95.74 % 96.70 % 96.22 % 37226 1269 1656 11.41 % 43622 1049

Benchmark MT PT ML IDS FRAG
CAR 86.92 % 11.54 % 1.54 % 22 197

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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