Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Multi-view approaches to people-tracking have the potential to better handleocclusions than single-view ones in crowded scenes. They often rely on thetracking-by-detection paradigm, which involves detecting people first and thenconnecting the detections. In this paper, we argue that an even more effectiveapproach is to predict people motion over time and infer people's presence inindividual frames from these. This enables to enforce consistency both overtime and across views of a single temporal frame. We validate our approach onthe PETS2009 and WILDTRACK datasets and demonstrate that it outperformsstate-of-the-art methods.