Non-supervised discovering of user activities in visual sensor networks for ambient intelligence applications

Ambient intelligence systems need to know what the users are doing. In this paper, An architecture for human activity recognition using a visual sensor network is proposed. The video sequence perceived by each camera is locally processed to obtain a local activity label. These activity labels are fused by an upper tier to obtain a global activity label. The activities recognized by the system are not specified a priori, they are discovered using automatic model selection techniques. Then, an expert has to label the discovered activities to give them a semantic meaning. Results of the application of the activity discovering procedure to a smart home dataset are shown.