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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Semi-supervised adapted HMMs for unusual event detection


    Contributors:
    Dong Zhang, (author) / Gatica-Perez, D. (author) / Bengio, S. (author) / McCowan, I. (author)


    Publication date :

    2005-01-01


    Size :

    249461 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    HMMs for Anomaly Detection in Autonomous Robots

    Davide Azzalini / Alberto Castellini / Matteo Luperto et al. | BASE | 2020

    Free access

    Semi-Supervised Face Detection

    Sebe, N. / Cohen, I. / Huang, T.S. et al. | IEEE | 2005


    3D surface analysis using coupled HMMs

    Pernkopf, F. | British Library Online Contents | 2005