Method

Fast Fusion Multi object Tracking [st] [la] [FFMOT]


Submitted on 10 Mar. 2023 14:14 by
Lu Yu (Tongji University)

Running time:0.01 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
This method is a simple tracking formulation that integrates object observations from both sensor modalities.
Parameters:
None
Latex Bibtex:
None

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 86.19 % 86.89 % 86.27 % 89.64 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 92.17 % 95.28 % 93.70 % 35091 1739 2983 15.63 % 41620 1316

Benchmark MT PT ML IDS FRAG
CAR 75.08 % 20.46 % 4.46 % 27 230

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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