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 HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 68.66 % 68.02 % 69.76 % 71.47 % 83.28 % 74.50 % 82.02 % 84.80 %

Benchmark TP FP FN
CAR 29092 5300 422

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 82.75 % 82.78 % 83.36 % 211 68.18 %

Benchmark MT rate PT rate ML rate FRAG
CAR 60.31 % 27.54 % 12.15 % 201

Benchmark # Dets # Tracks
CAR 29514 628

This table as LaTeX