Towards Detection and Tracking of On-Road Objects

Abstract

In this paper, we present a system capable of detecting and tracking on-road objects in the scene, in particular vehicles. Such a system is a useful part of a driver assistance system. This system employs two different techniques in the detection phase to increase the robustness. A large part of this paper is devoted to reducing the computational amount required of the overall algorithm by quickly excluding pixels above the horizon and on the road surface. The number of pixels that require further, computationally expensive processing is reduced by up to 65% in the sequences used in the experimental evaluation. Objects are detected in the remaining image areas by an improved boosting approach of weak classifiers based on the well-known AdaBoost and RealBoost approaches. The tracking is then done by a combination of periodically running the detection algorithm, while using adaptable templates at other times which allow for changes in shape and appearance as the car and the other vehicles travel along the road.