Optical flow based vehicle tracking strengthened by statistical decisions
Reliable tracking of cars from aerial video imagery is one of the main ingredients of microscopic traffic monitoring. Current tracking methods however are not able yet to track all the vehicles in all frames of video imagery taken by e.g. a helicopter. Several problem scenarios can be distinguished, like situations with many similar cars in congested traffic areas, cars that appear in low contrast compared to the background and cars that are occluded in some frames by other cars or by traffic signs. In this paper an improved method is described that continuously tracks all vehicles from their appearance into the viewing area until their exit. Our algorithm starts by separately tracking individual car pixels and the complete car region as a whole using the gradient-based optical flow method. A scale space approach is used to initiate the optical flow method. The best result as obtained from the intermediate results is used in the following statistical decision making step. Finally, either the best results are accepted and by applying a rigid body assumption, one displacement result is adapted for the car as a whole, or the best results are rejected, because even the best results fail a quality criterion. Continuation of these steps for all frames constitutes the final tracking result. This method solves most of the sketched problem scenarios as is illustrated by applying it on suited helicopter video imagery.
optical flow tracking statistic traffic monitoring scale space gradient based