A Novel Approach of Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes
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
How to Cite?: Dr. M. Kameshwara Rao, P. Bhavya Sree, "A Novel Approach of Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes", Volume 3 Issue 6, June 2014, International Journal of Science and Research (IJSR), Pages: 1204-1207, https://www.ijsr.net/getabstract.php?paperid=2014437, DOI: https://dx.doi.org/10.21275/2014437