Method

Learning Pseudo 3D Representation for Ego-centric 2D Multiple Object Tracking [P3DTrack]
[Anonymous Submission]

Submitted on 9 Aug. 2023 04:36 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Anonymous
Parameters:
Anonymous
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 86.06 % 84.71 % 86.73 % 87.73 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 91.28 % 96.62 % 93.88 % 34976 1224 3340 11.00 % 40885 834

Benchmark MT PT ML IDS FRAG
CAR 75.38 % 20.31 % 4.31 % 230 384

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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