Method

Learning to Track Targets by Rao-Blackwellized Particle Filtering [RBPF]
[Anonymous Submission]

Submitted on 19 Sep. 2016 15:22 by
[Anonymous Submission]

Running time:1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TBA
Parameters:
TBA
Latex Bibtex:
TBA

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 83.64 % 82.25 % 84.43 % 85.97 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 87.87 % 97.66 % 92.51 % 33045 791 4563 7.11 % 38013 1468

Benchmark MT PT ML IDS FRAG
CAR 64.77 % 29.38 % 5.85 % 273 651

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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