Method

RRC-IIITH [on] [RRC-IIITH]
[Anonymous Submission]

Submitted on 19 Sep. 2017 19:43 by
[Anonymous Submission]

Running time:0.3 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBA
Parameters:
TBA
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 84.24 % 85.73 % 85.60 % 88.62 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.80 % 97.95 % 93.15 % 33656 705 4247 6.34 % 38507 2382

Benchmark MT PT ML IDS FRAG
CAR 73.23 % 24.00 % 2.77 % 468 944

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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