Multi-view 3D vehicle tracking with a constrained filter

We present a vehicle tracker for automatic data collection at intersections and freeways. Key features of our approach are the ability to measure the real-world state of tracked vehicles, to hand off targets between cameras, and to track simultaneously from multiple views for improved handling of occlusions and scene clutter. Further, the proposed measurement model supports both full and partial measurements from any number of views, which are seamlessly fused by the estimation procedure. State constraints are incorporated in the tracking method, and issues caused by stop-and-go traffic and turning vehicles are addressed. The use of constrained estimation, the support of partial measurements, and the multi- view capability represents a significant improvement of our past efforts in automating the tracking of vehicles in challenging situations.