Method

DBTO3D [la] [gp] [on] [DBTO3D]
[Anonymous Submission]

Submitted on 27 Dec. 2018 07:36 by
[Anonymous Submission]

Running time:0.06 s
Environment:GPU @ >3.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
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 84.82 % 85.43 % 85.11 % 88.50 %
PEDESTRIAN 60.67 % 72.13 % 63.52 % 90.95 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.70 % 97.81 % 93.03 % 34198 766 4355 6.89 % 38012 830
PEDESTRIAN 72.39 % 89.72 % 80.13 % 17024 1950 6494 17.53 % 22399 483

Benchmark MT PT ML IDS FRAG
CAR 68.31 % 27.38 % 4.31 % 98 585
PEDESTRIAN 42.61 % 45.70 % 11.68 % 661 1659

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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