Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

Ruth Aguilar-Ponce (University of Louisana- Lafayette, USA), Ashok Kumar (University of Louisana- Lafayette, USA), J. Luis Tecpanecatl-Xihuitl (University of Louisana- Lafayette, USA), Magdy Bayoumi (University of Louisana- Lafayette, USA) and Mark Radle (University of Louisana- Lafayette, USA)
DOI: 10.4018/978-1-59904-249-7.ch011
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Abstract

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

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