International Journal of Image Processing and Pattern Recognition
A Sequence Structure for Image Change Detection Using Sensor Network
Abstract
Abstract Change detection in pictures is of nice interest due to its connectedness in several applications like video police work. This article work presents the underlying theoretical drawback of distributed image amendment detection employing a wireless detector network. The planned system consisted of multiple image sensors, which created native choices severally associate degreed send them to a foundation station through an additive white Gaussian noise channel. The bottom station then created a world call and declared whether or not a major amendment had occurred or not. This technique used four thresholds to sight native and world changes within the space being monitored. 2 thresholds outlined at the detector level helped the detector create a neighborhood call, and therefore the remaining 2 thresholds outlined at the system level helped the fusion center create a world call. Hence, by victimization four thresholds, the performance of the planned model was ascertained to own excellent fault tolerance. Keywords: change detection, qualitative sensor network, thresholdsReferences (7)
- Gay D., Levis P., Behren R.V., et al. The nesC Language: A holistic approach to networked embedded systems. In Proceedings of Programming Language Design and Implementation (PLDI); June 2003.
- M. Rahimi. Cyclops: Image Sensing and Interpretation in Wireless Sensor Networks. ACM Sensys Conference; November 2005.
- Collins R., Lipton A., Kanade T. Introduction to the special section on video surveillance. IEEE Trans. Pattern Anal. Mach. Intell. August 2000; 22(8): 745–6p.
- An Operating System for Networked Sensors. http://www.tinyos.net
- Stauffer C., Grimson W. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. August 2000; 22(8): 747–57p.
- Hampapur A., Brown L., Connell J., et al. Smart Video Surveillance. IEEE Signal Processing Magazine. March 2005; 38–51p.
- Kasetkasem T., Varshney P.K. An image change detection algorithm based on markov random field models. IEEE Trans. Geoscience and Remote Sensing. August 2002; 40(8): 887–902p.