Method

LXT-MOT [LXT-MOT]
[Anonymous Submission]

Submitted on 24 Aug. 2017 14:18 by
[Anonymous Submission]

Running time:0.3 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
private
Parameters:
detction confidence threshold = 0.4
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
PEDESTRIAN 39.16 % 72.31 % 40.17 % 92.71 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 45.99 % 89.64 % 60.79 % 10739 1241 12610 11.16 % 13733 223

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 14.43 % 50.17 % 35.40 % 233 905

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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