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
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
How to Cite this Article?
Dr. M. Kameshwara Rao, P. Bhavya Sree, "A Novel Approach of Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=2014437, Volume 3 Issue 6, June 2014, 1204 - 1207
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