Viewpoint independent detection of vehicle trajectories and lane geometry from uncalibrated traffic surveillance cameras

Abstract

In this paper, we present a low-level object tracking system that produces accurate vehicle trajectories and estimates the lane geometry using uncalibrated traffic surveillance cameras. A novel algorithm known as Predictive Trajectory Merge-and-Split (PTMS) has been developed to detect partial or complete occlusions during object motion and hence update the number of objects in each tracked blob. This hybrid algorithm is based on the Kaiman filter and a set of simple heuristics for temporal analysis. Some preliminary results are presented on the estimation of lane geometry through aggregation and K-means clustering of many individual vehicle trajectories modelled by polynomials of varying degree. We show how this process can be made insensitive to the presence of vehicle lane changes inherent in the data. An advantage of this approach is that estimation of lane geometry can be performed with non-stationary uncalibrated cameras. © Springer-Verlag 2004.