Java mining semantic context information for intelligent video surveillance of traffic scenes

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Ecway Technologies

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.
ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111
VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

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Java mining semantic context information for intelligent video surveillance of traffic scenes

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. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com