User-Centric Environment Discovery With Camera Networks in Smart Homes

We propose a data integration and reasoning technique for automatic environment discovery in smart homes based on observations of user interactions with objects. This approach is complementary to traditional appearance-based object recognition, which often demands large training sets. In our approach, object recognition is achieved in a semantic way by linking object behaviors to the pose and activity of the person using them. The complex relations between objects and user activities are modeled with a Markov logic network. The embodiments of the proposed approach in two multicamera smart environments are described, and the experimental results are presented.