Robust Multi‐person Tracking for Real‐Time Intelligent Video Surveillance

We propose a novel multiple‐object tracking algorithm for real‐time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two‐step pedestrian detection is employed to reduce the computat...

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Published inETRI journal Vol. 37; no. 3; pp. 551 - 561
Main Authors Choi, Jin‐Woo, Moon, Daesung, Yoo, Jang‐Hee
Format Journal Article
LanguageEnglish
Published Electronics and Telecommunications Research Institute (ETRI) 01.06.2015
한국전자통신연구원
Subjects
background modeling
Multiple‐object tracking
particle filter
pedestrian detection
real‐time applications
video surveillance applications
전자/정보통신공학
Online AccessGet full text
ISSN1225-6463
2233-7326
DOI10.4218/etrij.15.0114.0629

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Abstract We propose a novel multiple‐object tracking algorithm for real‐time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two‐step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two‐step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.
AbstractList We propose a novel multiple‐object tracking algorithm for real‐time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two‐step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two‐step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.
We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time. KCI Citation Count: 19
Author Moon, Daesung
Yoo, Jang‐Hee
Choi, Jin‐Woo
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  surname: Yoo
  fullname: Yoo, Jang‐Hee
  email: jhy@etri.re.kr
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Cites_doi 10.1016/j.sigpro.2009.03.034
10.1109/CVPR.2009.5206735
10.1109/CVPR.2006.195
10.1023/A:1008078328650
10.1155/2008/246309
10.1007/3-540-47969-4_44
10.1007/978-3-540-88688-4_58
10.1109/CVPR.2011.5995667
10.1007/978-1-4757-3437-9
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10.1109/CISP.2010.5646273
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Notes jwc@etri.re.kr
jhy@etri.re.kr
This work was partly supported by the ICT R&D program of the MSIP, Rep. of Korea (No. 10039149, Development of Basic Technology of Human Identification and Retrieval at Distance for Active Video Surveillance Service with Real‐time Awareness of Safety Threats) and (No. 13‐912‐06‐006, Development of the Filtering Technology for Objectionable Streaming Contents on Smart Platform).
Jin‐Woo Choi
daesung@etri.re.kr
and Jang‐Hee Yoo (corresponding author
are with the SW & Contents Research Laboratory, ETRI, Daejeon, Rep. of Korea.
Daesung Moon
G704-001110.2015.37.3.020
http://etrij.etri.re.kr/etrij/journal/article/article.do?volume=37&issue=3&page=551
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Snippet We propose a novel multiple‐object tracking algorithm for real‐time intelligent video surveillance. We adopt particle filtering as our tracking framework....
We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework....
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SubjectTerms background modeling
Multiple‐object tracking
particle filter
pedestrian detection
real‐time applications
video surveillance applications
전자/정보통신공학
Title Robust Multi‐person Tracking for Real‐Time Intelligent Video Surveillance
URI https://onlinelibrary.wiley.com/doi/abs/10.4218%2Fetrij.15.0114.0629
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