Abstract
This chapter proposes methods for tracking people and recognizing their actions through indoor scenes captured with an omnidirectional image sensor. They can be used to detect and track people to extract their trajectories of movement, and their actions are then recognized by using extracted trajectories. Recently, stochastic algorithms have frequently been used for action recognition because they require non-linear and non-Gaussian models of action. Action models prepared from trajectories, however, include movements that are almost the same, so they are redundant. The chapter, therefore, assumes that human actions can be classified into action primitives, which are modeled by transitions of discrete states considered as action primitives. The methods that are proposed combine continuous state models and discrete state models by stochastic sampling generated from state transition probabilities.
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