Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Adaptive Motion Segmentation for Automated Video Surveillance | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 187413 13 pages doi 2008 187413 Research Article An Adaptive Motion Segmentation for Automated Video Surveillance M. Ali Akber Dewan 1 M. Julius Hossain 2 and Oksam Chae1 1 Image Processing Lab Department of Computer Engineering KyungHee University Yongin-si Gyeonggi-do 446-701 South Korea 2 Centre for Image Processing and Analysis School of Electronic Engineering Dublin City University Glasnevin Dublin 9 Ireland Correspondence should be addressed to Oksam Chae oschae@ Received 4 April 2008 Revised 17 August 2008 Accepted 6 November 2008 Recommended by Dimitrios Tzovaras This paper presents an adaptive motion segmentation algorithm utilizing spatiotemporal information of three most recent frames. The algorithm initially extracts the moving edges applying a novel flexible edge matching technique which makes use of a combined distance transformation image. Then watershed-based iterative algorithm is employed to segment the moving object region from the extracted moving edges. The challenges of existing three-frame-based methods include slow movement edge localization error minor movement of camera and homogeneity of background and foreground region. The proposed method represents edges as segments and uses a flexible edge matching algorithm to deal with edge localization error and minor movement of camera. The combined distance transformation image works in favor of accumulating gradient information of overlapping region which effectively improves the sensitivity to slow movement. The segmentation algorithm uses watershed gradient information of difference image and extracted moving edges. It helps to segment moving object region with more accurate boundary even some part of the moving edges cannot be detected due to region homogeneity or other reasons during the detection step. Experimental results using different types of video sequences are .
