Method

Co-MOT [Co-MOT]


Submitted on 4 Jan. 2025 13:18 by
Xingdi Liu (Nanjing University of Posts and Telecommunications)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
In this paper, we propose a GNN based 3D MOT method
which effectively utilizes the collective motion
consistency in traffic flow.
Parameters:
Please check the paper.
Latex Bibtex:

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 85.54 % 85.52 % 86.58 % 88.52 %
PEDESTRIAN 47.87 % 64.89 % 48.81 % 88.98 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 92.76 % 94.40 % 93.57 % 33587 1991 2623 17.90 % 42713 2147
PEDESTRIAN 63.04 % 81.96 % 71.27 % 14695 3234 8616 29.07 % 21166 1182

Benchmark MT PT ML IDS FRAG
CAR 82.00 % 15.85 % 2.15 % 358 857
PEDESTRIAN 29.90 % 51.20 % 18.90 % 219 1348

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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