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

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This paper attempts to mine semantic context information from video surveillance of traffic scenes to build an intelligent system. It learns object-specific context about pedestrians and vehicles to classify them with a co-trained classifier. It also learns scene-specific context for each object type, like motion patterns, paths, and entry/exit points. This semantic context information improves object detection, tracking, and abnormal event detection. Experimental results show the effectiveness of these semantic context features for multiple traffic scenes.

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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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The document discusses mining semantic context information from traffic scenes to build an intelligent video surveillance system. It aims to extract object-specific context information and learn scene-specific context information from the object data. This allows the system to perform robust object detection, tracking, classification and abnormal event detection. The system uses co-trained classifiers to identify objects as pedestrians or vehicles with high accuracy. It also learns semantic scene context like motion patterns, width distributions, paths and entry/exit points for each object type. This context information helps improve detection, tracking and abnormal event identification. Experimental results show the effectiveness of the semantic context features for real-world traffic scenes.

Mining semantic context information for intelligent video surveillance of tra...

ecwayprojects

The document is an abstract that discusses mining semantic context information from traffic scenes to build an intelligent video surveillance system. It aims to learn object-specific context information and scene-specific context information to improve object detection, tracking, classification and abnormal event detection. A co-trained classifier is learned using multi-view object information to classify objects as pedestrians or vehicles with high accuracy while reducing labeled training samples. Motion patterns, width distributions, paths and entry/exit points are also learned as scene-specific context information for each object type to further aid object detection, tracking and abnormal event identification. Experimental results demonstrate the effectiveness of the semantic context features for multiple real-world traffic scenes.

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

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