Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 9, Issue 1, Pages 149-160Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2012.2218251
Keywords
Event detection; Gaussian mixture model (GMM) and graph cut; object classification; object detection; object tracking; video surveillance
Categories
Funding
- 973 Program [2012CB316304, 2010CB327905]
- National Natural Science Foundation of China [61070104, 6127239]
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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 scenes.
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