PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Supervised Adapted HMMs for Unusual Event Detection
Dong Zhang, Daniel Gatica-Perez, Samy Bengio and Iain McCowan
In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2005.

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Abstract

We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner. The proposed framework has an iterative structure, which adapts a new unusual event model at each iteration. We show that such a framework can address problems due to the scarcity of training data and the difficulty in pre-defining unusual events. Experiments on audio, visual, and audio-visual data streams illustrate its effectiveness, compared with both supervised and unsupervised baseline methods.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Multimodal Integration
ID Code:1102
Deposited By:Samy Bengio
Deposited On:26 September 2005

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