Method

YT[on][at] [YT]
[Anonymous Submission]

Submitted on 27 Jun. 2018 15:36 by
[Anonymous Submission]

Running time:0.03 s
Environment:4 cores @ >3.5 Ghz (Python)

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 49.76 % 75.69 % 50.26 % 81.94 %
PEDESTRIAN 36.90 % 71.22 % 38.06 % 91.15 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 61.75 % 90.09 % 73.28 % 23451 2579 14526 23.18 % 27473 1292
PEDESTRIAN 53.60 % 78.06 % 63.56 % 12506 3515 10825 31.60 % 17490 1068

Benchmark MT PT ML IDS FRAG
CAR 31.38 % 54.31 % 14.31 % 173 688
PEDESTRIAN 21.99 % 52.58 % 25.43 % 267 995

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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