Efficient region-based motion segmentation for a video monitoring system

被引:96
作者
Kim, JB [1 ]
Kim, HJ [1 ]
机构
[1] Kyungpook Natl Univ, Dept Comp Engn, Artificial Intelligence AI Lab, Puk Gu, Taegu 702701, South Korea
关键词
moving object detection and segmentation; object tracking; adaptive thresholding; k-means clustering; video monitoring system;
D O I
10.1016/S0167-8655(02)00194-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an efficient region-based motion segmentation method for segmentation of moving objects in a traffic scene with a focus on a video monitoring system (VMS). The presented method consists of two phases: first, in the motion detection phase, the positions of moving objects in a scene are determined using an adaptive thresholding method. To detect varying regions by moving objects, instead of determining the threshold value manually, we use an adaptive thresholding method to automatically choose the threshold value. Second, in the motion segmentation phase, pixels that have similar intensity and motion information are segmented using a weighted k-means clustering algorithm to the binary region of the motion mask obtained in the motion detection. In this way, we need not process a whole image so computation time is reduced. Experimental results demonstrate robustness not only in the variation of luminance conditions and changes in environmental conditions, but also for occlusions among multiple moving objects. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:113 / 128
页数:16
相关论文
共 19 条
[1]   Region-based parametric motion segmentation using color information [J].
Altunbasak, Y ;
Eren, PE ;
Tekalp, AM .
GRAPHICAL MODELS AND IMAGE PROCESSING, 1998, 60 (01) :13-23
[2]  
AMAMOTO N, 1999, ELECT COMM JPN 3, V82, P527
[3]   Segmenting traffic scenes from grey level and motion information [J].
Badenas J. ;
Bober M. ;
Pla F. .
Pattern Analysis & Applications, 2001, 4 (1) :28-38
[4]  
Bovik AC., 2000, HDB IMAGE VIDEO PROC
[5]   A real-time system for video surveillance of unattended outdoor environments [J].
Foresti, GL .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 1998, 8 (06) :697-704
[6]  
Gonzalez RC., 2006, DIGITAL IMAGE PROCES
[7]  
Irani M., 1993, Journal of Visual Communication and Image Representation, V4, P324, DOI 10.1006/jvci.1993.1030
[8]   A genetic algorithm-based segmentation of Markov random field modeled images [J].
Kim, EY ;
Park, SH ;
Kim, HJ .
IEEE SIGNAL PROCESSING LETTERS, 2000, 7 (11) :301-303
[9]   Spatiotemporal segmentation using genetic algorithms [J].
Kim, EY ;
Hwang, SW ;
Park, SH ;
Kim, HJ .
PATTERN RECOGNITION, 2001, 34 (10) :2063-2066
[10]  
Kim JB, 2001, IEEE REGION 10 INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONIC TECHNOLOGY, VOLS 1 AND 2, P313, DOI 10.1109/TENCON.2001.949604