Method

DH-TRK [DH-TRK]
[Anonymous Submission]

Submitted on 26 Oct. 2018 13:20 by
[Anonymous Submission]

Running time:0.1s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Multiple Object Detection and Tracking from
Surveillance Camera

Zhou XiangMing;Zhang HeQun;Zhang Peng;Chen Qing;
Parameters:
None
Latex Bibtex:
None

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 90.77 % 83.28 % 91.05 % 86.29 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.02 % 98.87 % 95.85 % 35577 408 2671 3.67 % 41552 1044

Benchmark MT PT ML IDS FRAG
CAR 80.62 % 16.00 % 3.38 % 96 232

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.


eXTReMe Tracker