Activity Recognition Using Probabilistic Timed Automata

Activity recognition focuses on what is happening in the scene. It endeavors to recognize the actions and goals of one or more actors from a sequence of observations both on the actor actions and the environmental conditions. Automated recognition of human activity is essential ability that may be used in the surveillance to provide security in indoor as well as outdoor environments. Understanding human activity is also important for human-computer-interaction systems including tele-conferencing and for content-based retrieval of video from digital repositories. The main technique utilized in activity recognition is computer vision. In vision-based activity recognition, a great deal of work has already been done. This is partially due to increasing computational power that allows huge amount of video to be processed and stored, but also due to the large number of potential applications. In vision-based activity recognition, we can distinguish four steps, namely human detection, human tracking, human activity recognition and then a high-level activity evaluation. A method of (Viola et al., 2003) detects a moving pedestrian in a temporal sequence of images. A linear combination of filters is applied to compute motion and appearance features that are then summed to determine a cumulative score, employed afterwards in a classification of the detection window as including the moving object. For vision based activity recognition, tracking is the fundamental component. The entity must be first tracked before the recognition can take place. Briefly, the goal of visual tracking is to find and describe the relative position change of the moving object from one frame to another in the whole sequence, while the task of action recognition is to classify the person’s action given the person’s location, recent appearance, etc. Kalman filters (Crowley & Berard, 1997; Kwolek, 2003) and particle filtering–based algorithms (Nait-Charif & McKenna, 2003) are utilized extensively for object tracking in this domain. These algorithms generally involve an object state transition model and an observation model, which reflect both motion and appearance of the object (Haykin & de Freitas, 2004). After tracking of the moving objects the action recognition stage occurs, where Dynamic Time Warping (Myers et al., 1980; Myers & Rabiner, 1981) and Hidden Markov Models (Brand & Kettnaker, 2000) are employed very often at this stage. Sophisticated stochastic models such as Dynamic Bayesian Networks (Albrecht et al., 1997; Ghahramani, 1997), Stochastic Context Free