Moving object detection in dynamic environment

Authors:
M. Julius Hossain;Kiok Ahn;June Hyung Lee;Oksam Chae
Affiliations:
Department of Computer Engineering, Kyung Hee University, Yongin-si, Kyunggi-do, Korea;Department of Computer Engineering, Kyung Hee University, Yongin-si, Kyunggi-do, Korea;Department of Computer Engineering, Kyung Hee University, Yongin-si, Kyunggi-do, Korea;Department of Computer Engineering, Kyung Hee University, Yongin-si, Kyunggi-do, Korea
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 4
Cited 2

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination independent change detection for real world image sequences

Computer Vision, Graphics, and Image Processing
A framework for constructing features and models for intrusion detection systems

ACM Transactions on Information and System Security (TISSEC)
Digital Image Processing

Digital Image Processing

Suitability of edge segment based moving object detection for real time video surveillance

KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Particle filter based tracking of moving object from image sequence

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an edge extraction based automatic algorithm for detection of moving objects that have been specially developed to deal with the variations in illumination and background. We develop an efficient approach for background edge generation as well as update and robust method of edge matching that is able to effectively reduce the risk of false alarm. The proposed method can be successfully realized in various monitoring systems like intrusion detection as well as video surveillance. Experiments with real image sequences are presented, along with comparisons with some other existing methods, illustrating the advantages of the proposed algorithm.