Activity Modeling Using Event Probability Sequences

Changes in motion properties of trajectories provide useful cues for modeling and recognizing human activities. We associate an event with significant changes that are localized in time and space, and represent activities as a sequence of such events. The localized nature of events allows for detection of subtle changes or anomalies in activities. In this paper, we present a probabilistic approach for representing events using the hidden Markov model (HMM) framework. Using trained HMMs for activities, an event probability sequence is computed for every motion trajectory in the training set. It reflects the probability of an event occurring at every time instant. Though the parameters of the trained HMMs depend on viewing direction, the event probability sequences are robust to changes in viewing direction. We describe sufficient conditions for the existence of view invariance. The usefulness of the proposed event representation is illustrated using activity recognition and anomaly dete...

• Type: Journal
• Year: 2008
• Where: TIP
• Authors: Naresh P. Cuntoor, B. Yegnanarayana, Rama Chellappa
• Keywords: Event Probability Sequences, TIP 2008, Trained Hmms, Viewing Direction