Method

Densebox-HM[on] [DBHM*]
[Anonymous Submission]

Submitted on 12 Jul. 2016 04:30 by
[Anonymous Submission]

Running time:0.15 s
Environment:4 cores @ 2.5 Ghz (C/C++)

Method Description:
Detection is provided by Our Densebox
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 66.22 % 77.72 % 70.75 % 82.18 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 85.02 % 88.19 % 86.57 % 32435 4345 5715 39.06 % 42529 3324

Benchmark MT PT ML IDS FRAG
CAR 52.15 % 39.69 % 8.15 % 1557 2060

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