Method

A New Robust and Efficient 3D Multi-Object Tracker for Point Clouds[la][on] [RE3T]
[Anonymous Submission]

Submitted on 16 Dec. 2019 04:38 by
[Anonymous Submission]

Running time:0.0045 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
A New Robust and Efficient 3D Multi-Object Tracker for
Point Clouds
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 88.89 % 84.36 % 88.98 % 87.72 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 92.09 % 97.81 % 94.86 % 34991 785 3005 7.06 % 41206 698

Benchmark MT PT ML IDS FRAG
CAR 78.92 % 10.31 % 10.77 % 31 193

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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