Method

SCMOT: Improving 3D Multi-Object Tracking via Semantic Inference and Confidence Optimization [SCMOT]


Submitted on 24 Oct. 2023 08:44 by
Lin Zhao (Beijing Institute of Technology)

Running time:0.01 s
Environment:2 cores @ 2.5 Ghz (Python)

Method Description:
a multi-modal 3D MOT framework
Parameters:
nan
Latex Bibtex:
nan

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 80.34 % 78.15 % 83.27 % 83.50 % 85.01 % 86.85 % 90.16 % 87.56 %

Benchmark TP FP FN
CAR 32577 1815 1205

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.80 % 86.16 % 91.22 % 144 77.69 %

Benchmark MT rate PT rate ML rate FRAG
CAR 84.31 % 9.85 % 5.85 % 91

Benchmark # Dets # Tracks
CAR 33782 787

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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