Method

DuEye[on] [DuEye]
[Anonymous Submission]

Submitted on 19 Aug. 2016 07:41 by
[Anonymous Submission]

Running time:0.15 s
Environment:1 core @ >3.5 Ghz (C/C++)

Method Description:
Use detetions from our method.
Parameters:
N/A
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 80.64 % 83.52 % 81.68 % 86.73 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 85.24 % 97.54 % 90.97 % 31760 802 5500 7.21 % 36086 1718

Benchmark MT PT ML IDS FRAG
CAR 61.85 % 32.31 % 5.85 % 356 991

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