Open AccessRobust Multi‐person Tracking for Real‐Time Intelligent Video Surveillance
Author(s) -
Choi JinWoo,
Moon Daesung,
Yoo JangHee
Publication year - 2015
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.15.0114.0629
Subject(s) - background subtraction , computer science , video tracking , artificial intelligence , tracking (education) , particle filter , computer vision , pedestrian detection , computation , bittorrent tracker , object detection , matching (statistics) , real time computing , kalman filter , object (grammar) , algorithm , pedestrian , pixel , pattern recognition (psychology) , eye tracking , engineering , mathematics , psychology , pedagogy , statistics , transport engineering
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.