Method

IMMDP[on] [IMMDP]


Submitted on 23 Nov. 2016 08:26 by
Zuanxu Gong (University of Science and Technology of China)

Running time:0.19 s
Environment:4 cores @ >3.5 Ghz (Matlab + C/C++)

Method Description:
An improvement of past MDP method.
Parameters:
Latex Bibtex:
@inproceedings{Xiang2015ICCV,
author = {Xiang, Yu and Alahi, Alexandre
and
Savarese, Silvio},
title = {Learning to Track: Online Multi-
Object Tracking by Decision Making},
booktitle = {International Conference on
Computer Vision (ICCV)},
pages = {4705--4713},
year = {2015}
}
@inproceedings{Ren2015NIPS,
author = {Shaoqing Ren and
Kaiming He and
Ross B. Girshick and
Jian Sun},
title = {Faster {R-CNN:} Towards Real-
Time
Object Detection with Region Proposal
Networks},
booktitle = NIPS,
year = {2015},
url =
{http://arxiv.org/abs/1506.01497},
timestamp = {Wed, 01 Jul 2015 15:10:24
+0200},
biburl = {http://dblp.uni-
trier.de/rec/bib/journals/corr/RenHG015},
bibsource = {dblp computer science
bibliography, http://dblp.org}
}

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 83.04 % 82.74 % 83.54 % 86.58 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 86.11 % 98.82 % 92.03 % 32668 391 5269 3.51 % 35646 701

Benchmark MT PT ML IDS FRAG
CAR 60.62 % 28.00 % 11.38 % 172 365

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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