Method

[on]MBKF [MBKF]
[Anonymous Submission]

Submitted on 10 Oct. 2017 07:44 by
[Anonymous Submission]

Running time:0.01 s
Environment:GPU @ 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 69.77 % 83.03 % 70.96 % 87.25 %
PEDESTRIAN 42.88 % 72.33 % 45.05 % 91.53 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 76.72 % 95.83 % 85.21 % 28776 1253 8734 11.26 % 32174 680
PEDESTRIAN 62.12 % 78.82 % 69.48 % 14481 3892 8830 34.99 % 22169 480

Benchmark MT PT ML IDS FRAG
CAR 41.23 % 47.38 % 11.38 % 410 971
PEDESTRIAN 27.49 % 51.55 % 20.96 % 501 1430

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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