Transferring activities: Updating human behavior analysis

One of the great open challenges in visual recognition is the ability to cope with unexpected stimuli. In this work, we present a technique to interpret detected anomalies and update the existing knowledge of normal situations. The addressed context is the analysis of human behavior in indoor surveillance scenarios, where new activities might need to be learned, once the system is already in operation. Our approach is based on human tracking with multiple activity trackers. The main contribution is to integrate a learning stage, where labeled and unlabeled information is collected and analyzed. To this end we develop a new multi-class version of transfer learning which requires minimal human interaction but still provides semantic labels of the new classes. The activity model is then updated with the new activities. Experiments show promising results.