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M.Tech / M.E / PhD Thesis | Electronics & Communication Engineering | India | Volume 3 Issue 6, June 2014
A Novel Approach of Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes
Dr. M. Kameshwara Rao | P. Bhavya Sree
Abstract: Automated visual surveillance systems are attracting extensive interest due to public security. In this paper; we attempt to mine semantic context information including object-specific context information and scene-specific context information (learned from object-specific context information) to build an intelligent system with robust object detection; tracking; and classification and abnormal event detection. By means of object-specific context information; a cotrained classifier; which takes advantage of the multiview information of objects and reduces the number of labeling training samples; is learned to classify objects into pedestrians or vehicles with high object classification performance. For each kind of object; we learn its corresponding semantic scene specific context information: motion pattern; width distribution; paths; and entry/exist points. Based on this information; it is efficient to improve object detection and tracking and abnormal event detection. Experimental results demonstrate the effectiveness of our semantic context features for multiple real-world traffic.
Keywords: object detection, object tracking, abnormal event detection, morphological operation Estimation and video surveillance
Edition: Volume 3 Issue 6, June 2014,
Pages: 1204 - 1207
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