Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Multi-modal emotive computing in a smart house environment
Pervasive and Mobile Computing
Visual surveillance using less ROIs of multiple non-calibrated cameras
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy. The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.