Modeling Human Behaviour: from Activities to Scene Understanding

This paper concerns human behaviour under- standing. The aim is to jointly use information from the environment spatial aordances and the observed activities performed by people. Starting from a recognizable set of four basic activities, {Active, Inactive, Walking, Running}, spa- tial regions of interest are identified. These regions are de- fined by a mixture of Gaussians, estimated using the Ex- pectation Maximization algorithm. The defined regions end up containing specific transitions between the basic activi- ties and are grouped in three dierent classes: {Entry/Exit, Target, Neutral}. The environment can then be modeled as a Finite-State Automata where each state (region) oers dif- ferent aordances. This spatial aordances are further mod- eled as Stochastic Timed Automatas. A hierarchical model with two levels is thus obtained and two possible applica- tions are sugested, in order to improve the activity classifi- cation and generate scene descriptions.