Method

Multi-Hierarchy Fusion and Occlusion Estimator for Multiple Object Tracking [on] [MHF-SOE]
[Anonymous Submission]

Submitted on 1 Nov. 2023 13:07 by
[Anonymous Submission]

Running time:1247 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Multi-Hierarchy Fusion for feature association; SOE
for location association.
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 86.61 % 81.65 % 87.02 % 85.46 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.99 % 97.24 % 94.01 % 35022 994 3469 8.94 % 42208 839

Benchmark MT PT ML IDS FRAG
CAR 73.38 % 22.62 % 4.00 % 142 444

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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