Semi-supervised adapted HMMs for unusual event detection

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 611 - 618 vol. 1
• Main Authors: Dong Zhang, Gatica-Perez, D., Bengio, S., McCowan, I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Ambient intelligence; Bayesian methods; Computer vision; Data mining; Event detection; Hidden Markov models; Information management; Streaming media; Surveillance; Training data
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.316

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 audiovisual data streams illustrate its effectiveness, compared with both supervised and unsupervised baseline methods.