Vehicle tracking and distance estimation based on multiple image features

In this paper, we introduce a vehicle tracking algorithm based on multiple image features to detect and track the front car in a collision avoidance system (CAS) application. The algorithm uses multiple image features, such as corner, edge, gradient, vehicle symmetry property, and image matching technique to robustly detect the vehicle bottom corners and edges, and estimate the vehicle width. Based on the estimated vehicle width, a few pre-selected edge templates are used to match the image edges that allow us to estimate the vehicle height, and also the distance between the front vehicle and the host vehicle. Some experimental results based on real world video images are presented. These seem to indicate that the algorithm is capable of identifying a front vehicle, tracking it, and estimating its distance from the host vehicle.