Semi-supervised Adapted HMMs for Unusual Event Detection
| Type of publication: | Idiap-RR |
| Citation: | zhang-rr-04-28 |
| Number: | Idiap-RR-80-2004 |
| Year: | 2004 |
| Institution: | IDIAP |
| Address: | Martigny, Switzerland |
| Note: | Published in ``Prof. IEEE CVPR'', June, 2005 |
| 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. |
| Userfields: | ipdinar={2004}, ipdmembership={vision}, language={English}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Crossref by |
zhang-rr-04-28b |
| Added by: | [UNK] |
| Total mark: | 0 |
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