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1. MINING SEMANTIC CONTEXT INFORMATION FOR INTELLIGENT VIDEO
SURVEILLANCE OF TRAFFIC SCENES
ABSTRACT:
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 multi view 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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