Stereovision Based Vehicle Tracking in Urban Traffic Environments

This paper presents an algorithm for tracking the cuboids generated from grouping the 3D points obtained through stereovision. The solution described in the paper takes into consideration the particularities of the scenario and of the sensor, and brings considerable improvement in all the phases of tracking: initialization, prediction, measurement and update. The corner of the cuboid becomes the central working concept, thus improving the handling of partially occluded objects, of objects partially out of the field of view, and of objects whose measurement is fragmented by the sensor inaccuracies. After association at corner level, multiple measurements or validated parts of a measurement form a virtual object, the meta measurement, which is used for track update. The size of a vehicle is tracked using a histogram voting method. The resulted algorithm shows robustness and accuracy in the crowded urban scenario.