A probabilistic approach to incorporating domain knowledge for closed-room people monitoring

We propose a novel probabilistic approach to recognizing people entering and leaving a closed room in human work place or living environment. Specifically, people in the view of a monitoring camera are first tracked and represented using low-level color features. Based on a new color similarity measure, optimal recognition of people leaving and entering the room is carried out by probabilistic reasoning under the constraints imposed by the domain knowledge, e.g., a person currently inside a room cannot enter again without first leaving it, and vice versa. The novelty of our work mainly lies in the development of a systematic way to incorporate the correlation and constraint among a sequence of people observations, and the optimality of recognition is achieved by maximizing a joint posterior probability of the observations. Experimental results of real and synthetic data are presented to show the efficacy of the proposed approach.