Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Retrieving and visualizing video
Communications of the ACM
Unsupervised video segmentation and object tracking
Computers in Industry - Special issue on advances in computer integrated production in honour of professor C.L. Moodie's retirement
Model-based varying pose face detection and facial feature registration in video images
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Digital Image Processing
A Spatio-Temporal Semantic Model for Multimedia Database Systems and Multimedia Information Systems
IEEE Transactions on Knowledge and Data Engineering
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
A Fast Background Scene Modeling and Maintenance for Outdoor Surveillance
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Object tracking and multimedia augmented transition network for video indexing and modeling
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
An algorithm to estimate mean traffic speed using uncalibrated cameras
IEEE Transactions on Intelligent Transportation Systems
Image analysis and rule-based reasoning for a traffic monitoring system
IEEE Transactions on Intelligent Transportation Systems
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The analysis and mining of traffic video sequences to discover important but previously unknown knowledge such as vehicle identification, traffic flow, queue detection, incident detection, and the spatio-temporal relations of the vehicles at intersections, provide an economic approach for daily traffic monitoring operations. To meet such demands, a multimedia data mining framework is proposed in this paper. The proposed multimedia data mining framework analyzes the traffic video sequences using background subtraction, image/video segmentation, vehicle tracking, and modeling with the multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over traffic intersections. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-à-vis traffic operations. Three real-life traffic video sequences obtained from different sources and with different weather conditions are used to illustrate the effectiveness and robustness of the proposed multimedia data mining framework by demonstrating how the proposed framework can be applied to traffic applications to answer the spatio-temporal queries.