Learning People Movement Model from Multiple Cameras for Behaviour Recognition

Abstract. In surveillance systems for monitoring people behaviour, it is imporant to build systems that can adapt to the signatures of the people tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance sysgned for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is b...

• Type: Conference
• Year: 2004
• Where: SSPR
• Authors: Nam Thanh Nguyen, Svetha Venkatesh, Geoff A. W. West, Hung Hai Bui
• Keywords: Behaviours, Complex Human Behaviours, Pattern Recognition, People Behaviours, SSPR 2004