Background modeling using phase space for day and night video surveillance systems

Authors:
Yu-Ming Liang;Arthur Chun-Chieh Shih;Hsiao-Rong Tyan;Hong-Yuan Mark Liao
Affiliations:
Department of Computer Sciene and Information Engineering, National Chiao-Tung University, Taiwan;Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan;Department of Computer Engineering, Chung-Yuan Christian University, Taiwan;Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan
Venue:
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Year:
2004

Citing 4
Cited 1

Curves and surfaces in computer aided geometric design

Curves and surfaces in computer aided geometric design
Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Applying an elitist electromagnetism-like algorithm to head robot stabilization

ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel background modeling approach for day and night video surveillance. A great number of background models have been proposed to represent the background scene for video surveillance. In this paper, we propose a novel background modeling approach by using the phase space trajectory to represent the change of intensity over time for each pixel. If the intensity of a pixel which originally belongs to the background deviates from the original trajectory in phase space, then it is considered a foreground object pixel. In this manner, we are able to separate the foreground object from the background scene easily. The experimental results show the feasibility of the proposed background model.