Surveillance systems aim to characterize human activities and to detect abnormal behaviors. This task is specially challenging if the camera field of view is wide and the objects are far from the camera.
In such operating conditions, it is not possible to extract detailed descriptions of the objects such as shape and color. In this case, most of the information is conveyed by the object trajectory and motion parameters. We therefore need to characterize trajectories and to be able to discriminate normal from abnormal behaviors.
This talk presents a new representation for human activity analysis based on multiple motion fields, equipped with space-varying switching mechanisms. We will show that this description is flexible and intuitive. The model parameters have a meaning and they can be used to understand how people behave in a scene. Parameter estimation will be addressed using the EM method and several extensions will be discussed.