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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.

This paper is concerned with the problem of estimating the motion of a single camera from a sequence of images, with an application scenario of vehicle egomotion estimation. Egomotion estimation has been an active area of research for many years and various solutions to the problem have been proposed. Many methods rely on optical flow or local image features to establish the spatial relationship between two images. A new method of egomotion estimation is presented which makes use of the Fourier-Mellin Transform for registering images in a video sequence, from which the rotation and translation of the camera motion can be estimated. The Fourier-Mellin Transform provides an accurate and efficient way of computing the camera motion parameters. It is a global method that takes the contributions from all pixels into account. The performance of the proposed approach is compared to two variants of optical flow methods and results are presented for a real-world video sequence taken from a moving vehicle.