Using Homographies for Vehicle Motion Estimation

Abstract: This master’s thesis describes a way to represent vehicles when tracking them through an image sequence. Vehicles are described with a state containing their position, velocity, size, etc.. The thesis highlights the properties of homographies due to their suitability for estimation of projective transformations. The idea is to approximatively represent vehicles with planes based on feature points found on the vehicles. The purpose with this approach is to estimate the displacement of a vehicle by estimating the transformation of these planes. Thus, when avehicle is observed from behind, one plane approximates features found on the back and one plane approximates features found on the side, if the side of the vehicle is visible. The projective transformations of the planes are obtained by measuring the displacement of feature points. The approach presented in this thesis builds on the prerequisites that a camera placed on a vehicle provides an image of its field of view. It does not cover how to find vehicles in an image and thus it requires that the patch which contains the vehicle is provided. Even though this thesis covers large parts of image processing functionalities, the focus is on how to represent vehicles and how to design an appropriate filter for improving estimates of vehicle displacement. Due to noisy features points, approximation of planes, and estimated homographies, the obtained measurements are likely to be noisy. This requires a filter that can handle corrupt measurements and still use those that are not. An unscented Kalman filter, UKF, is utilized in this implementation. The UKF is an approximate solution to nonlinear filtering problems and is here used to update the vehicle’s states by using measurements obtained from homographies. The choice of the unscented Kalman filter was made because of its ease of implementation and its potentially good performance. The result is not a finished implementation for tracking of vehicles, but rather a first attempt for this approach. The result is not better than the existing approach, which might depend on one or several factors such as poorly estimated homographies, unreliable feature points and bad performance of the UKF.