Method

BEV-Tracking [la][on] [BEV-Tracking]


Submitted on 22 Jun. 2020 05:34 by
sz hou (none)

Running time:0.03 s
Environment:4 cores @ >3.5 Ghz (Python)

Method Description:
BEV-Tracking
A 3D multitarget tracking scheme based on BEV map.
Parameters:
BEV-Tracking
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 66.02 % 85.81 % 71.51 % 89.31 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 79.83 % 93.50 % 86.13 % 30411 2115 7682 19.01 % 35472 1491

Benchmark MT PT ML IDS FRAG
CAR 45.69 % 45.85 % 8.46 % 1889 2578

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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