RETRIEVAL OF VEHICLE TRAJECTORIES AND ESTIMATION OF LANE GEOMETRY USING NON-STATIONARY TRAFFIC SURVEILLANCE CAMERAS
José Melo, Andrew Naftel, Alexandre J. M. Bernardino, José Santos-Victor · 2004
A tracking system is presented for obtaining accurate vehicle trajectories using uncalibrated traffic surveillance cameras. Techniques for indexing and retrieval of vehicle trajectories and estimation of lane geometry are also presented. An algorithm known as Predictive Trajectory Merge-and-Split (PTMS) is used to detect partial or complete occlusions during object motion. This hybrid algorithm is based on the constant acceleration Kalman filter and a set of simple heuristics for temporal analysis. The resulting vehicle trajectories are modeled using variable low-order polynomials. A comparative evaluation of several distance metrics used in trajectory cluster analysis, indexing and retrieval is also presented. We propose some changes to metrics presented in previous work and make a comparative study with a modified form of the Hausdorff distance. Some preliminary results are presented on the estimation of lane geometry through K-means clustering of individual vehicle trajectories using the proposed metrics. An advantage of our approach is that estimation of lane geometry can be performed with non-stationary, uncalibrated traffic cameras in real time. 1.