Shape quantization and recognition with randomized trees
Neural Computation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Automatic Learning Techniques in Power Systems
Machine Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
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International Journal of Computer Vision
Machine Learning
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WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
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IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
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This paper presents a classifier-based approach to recognize events in video surveillance sequences. The aim of this work is to propose a generic event recognition system that can be used without relying on a long-term tracking procedure. It is composed of three stages. The first one aims at defining and building a set of relevant features from the foreground objects. Second, a clustering tree-based method is used to handle the features and aggregate them locally in a set of coarse to fine activity patterns. Finally, events are modeled as a sequence of structured patterns with an ensemble of randomized trees. In particular, we want this classifier to discover the temporal and causal correlations between the most discriminative patterns. Our system is tested on simulated events and in a real world context with the CAVIAR video sequences dataset. Preliminary results demonstrate the effectiveness of the proposed framework for event recognition in automated visual surveillance applications. We also prove that more flexible algorithms (i.e. deterministic classifiers) rather than probabilistic graph models are conceivable for video events analysis.