Method

Low Computational Scheme for Online Multi-object Tracking in Autonomous Driving Environments [on] [LCS_MOT]
[Anonymous Submission]

Submitted on 22 Jul. 2019 08:50 by
[Anonymous Submission]

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBA
Parameters:
\theta_S=0.6, \theta_F=0.2
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 84.94 % 85.51 % 85.42 % 88.16 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.46 % 98.03 % 93.00 % 33305 670 4344 6.02 % 40154 1328

Benchmark MT PT ML IDS FRAG
CAR 72.77 % 23.38 % 3.85 % 165 627

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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