Efficient tracking of many objects in structured environments

We consider the special case of tracking objects in highly structured scenes. In the context of vehicle tracking in urban environments, we offer a fully automatic, end-to-end system that discovers and parametrizes the lanes along which vehicles drive, then uses just these pixels to simultaneously track dozens of objects. This system includes a novel active contour energy function used to parametrize the lanes of travel based only on the accumulation of spatio-temporal image derivatives, and a tracking algorithm that exploits longer temporal constraints made possible by our compact data representation; we believe both of these may be of independent interest. We offer quantitative results comparing tracking results to ground-truthed data, including thousands of vehicles from the NGSIM Peachtree data set.