Method

Fast Metric Multi-Object Vehicle Tracking [FMMOVT] [on] [FMMOVT V2]
[Anonymous Submission]

Submitted on 9 Mar. 2016 13:43 by
[Anonymous Submission]

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Testing
Parameters:
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
CAR 39.40 % 80.05 % 41.10 % 87.14 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 48.93 % 88.54 % 63.03 % 17265 2234 18022 20.08 % 20113 2699

Benchmark MT PT ML IDS FRAG
CAR 21.08 % 47.85 % 31.08 % 585 1122

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