Method

CrossTracker [CrossTracker]


Submitted on 5 Sep. 2023 15:42 by
wenqi lu (Nanjing University of Aeronautics and Astronautics)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
a multi-modal MOT method
Parameters:
TBD
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 92.05 % 87.26 % 92.22 % 89.70 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.36 % 98.74 % 96.50 % 36882 472 2205 4.24 % 43219 931

Benchmark MT PT ML IDS FRAG
CAR 85.08 % 12.31 % 2.62 % 56 195

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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