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 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 90.90 % 86.31 % 91.28 % 88.84 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 95.26 % 96.81 % 96.03 % 36247 1194 1804 10.73 % 42950 883

Benchmark MT PT ML IDS FRAG
CAR 84.46 % 9.69 % 5.85 % 130 197

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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