Method

Tracking by Detection with Convolution Sharing: A Deep Multi-vehicle Tracking System [on] [TDCS]
[Anonymous Submission]

Submitted on 26 Jan. 2016 16:56 by
[Anonymous Submission]

Running time:0.06 s
Environment:1 core @ 2.0 Ghz (Matlab + C/C++)

Method Description:
Tracking by Detection with Convolution Sharing
Parameters:
score = 0.6
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 55.38 % 75.20 % 55.72 % 81.22 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 63.08 % 94.39 % 75.62 % 23614 1404 13824 12.62 % 27133 1035

Benchmark MT PT ML IDS FRAG
CAR 23.23 % 54.92 % 21.85 % 118 961

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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