Improving multiple target tracking in structured environments using velocity priors

In this paper, we present an algorithm for determining a velocity probability distribution prior from low frame rate aerial video of an urban area, and show how this may be used to aid in the multiple target tracking problem, as well as to provide a foundation for the automated classification of urban transportation infrastructure. The algorithm used to develop the prior is based on using a generic interest point detector to find automobile candidate locations, followed by a series of filters based on scale and motion to reduce the number of false alarms. The remaining locations are then associated between frame pairs using a simple matching algorithm, and the corresponding tracks are then used to build up velocity histograms in the areas that are moved through between the track endpoints. The algorithm is tested on a dataset taken over urban Tucson, AZ. The results demonstrate that the velocity probability distribution prior can be used to infer a variety of information about road lane directions, speed limits, etc..., as well as providing a means of describing environmental knowledge about traffic rules that can be used in tracking.