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 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 81.94 % 79.09 % 85.55 % 84.43 % 85.39 % 88.09 % 91.69 % 87.95 %

Benchmark TP FP FN
CAR 32726 1666 1279

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.32 % 86.71 % 91.44 % 41 78.67 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.77 % 11.38 % 1.85 % 219

Benchmark # Dets # Tracks
CAR 34005 784

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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