Method

Loc Phenikaa-X [Loc Phenikaa-X]
[Anonymous Submission]

Submitted on 20 Dec. 2022 04:07 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBD
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 86.48 % 86.77 % 86.89 % 89.60 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.97 % 96.92 % 93.85 % 34412 1093 3417 9.83 % 39784 1334

Benchmark MT PT ML IDS FRAG
CAR 76.46 % 20.62 % 2.92 % 139 692

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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