Method

NECMA [NECMA]
[Anonymous Submission]

Submitted on 17 Jul. 2017 20:29 by
[Anonymous Submission]

Running time:0.5 s
Environment:8 cores @ 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 84.98 % 83.14 % 85.08 % 87.42 %
PEDESTRIAN 42.67 % 72.51 % 42.89 % 92.57 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.70 % 97.90 % 93.07 % 34487 738 4395 6.63 % 40860 775
PEDESTRIAN 55.98 % 81.64 % 66.42 % 13076 2941 10281 26.44 % 17625 274

Benchmark MT PT ML IDS FRAG
CAR 70.77 % 20.15 % 9.08 % 33 162
PEDESTRIAN 30.58 % 30.24 % 39.18 % 49 529

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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