Persistence and tracking: Putting vehicles and trajectories in context

City-scale tracking of all objects visible in a camera network or aerial video surveillance is an important tool in surveillance and traffic monitoring. We propose a framework for human guided tracking based on explicitly considering the context surrounding the urban multi-vehicle tracking problem. This framework is based on a standard (but state of the art) probabilistic tracking model. Our contribution is to explicitly detail where human annotation of the scene (e.g. ¿this is a lane¿), a track (e.g. ¿this track is bad¿), or a pair of tracks (e.g. ¿these two tracks are confused¿) can be naturally integrated within the probabilistic tracking framework. For an early prototype system, we offer results and examples from a dense urban traffic camera network tracking, querying data with thousands of vehicles over 30 minutes.