<p>This master's thesis develops an algorithm for tracking of cars robust enough to handle turning cars. It is implemented in the image processing environment Image Processing Application Programming Interface (IPAPI) for use with the WITAS project. </p><p>Firstly, algorithms, comparable with one currently used in the WITAS-project, are studied. The focus is on how rotation, that originates from the turning of the cars, affects tracking performance. The algorithms studied all perform an exhaustive search over a region, close to the last known position of the object being tracked, to find a match. After this, an iterative algorithm, based on the idea that a car can only rotate, translate and change scale, is introduced. The algorithm estimates the parameters describing this rotation, translation, and change of scale, iteratively. The iterative process needs a initial parameter estimate that is accurate enough for the algorithm to converge. The developed algorithm is based on an earlier publication on the subject, however the mathematical description, and deduction, of it is taken one step further than in this publication. </p><p>The iterative algorithm used performs well under the assumption that the data used fulfills some basic criteria. These demands comprises: placement of camera, template size as well as how the parameters may vary between two observations. The iterative algorithm is also potentially faster than exhaustive search methods, because few iterations are needed when the parameters change slowly. Better initial parameters should improve stability and speed of convergation. Other suggestions that could give better performance is discussed, e.g., methods to better extract the target from the surroundings.</p>

This master's thesis develops an algorithm for tracking of cars robust enough to handle turning cars. It is implemented in the image processing environment Image Processing Application Programming Interface (IPAPI) for use with the WITAS project. Firstly, algorithms, comparable with one currently used in the WITAS-project, are studied. The focus is on how rotation, that originates from the turning of the cars, affects tracking performance. The algorithms studied all perform an exhaustive search over a region, close to the last known position of the object being tracked, to find a match. After this, an iterative algorithm, based on the idea that a car can only rotate, translate and change scale, is introduced. The algorithm estimates the parameters describing this rotation, translation, and change of scale, iteratively. The iterative process needs a initial parameter estimate that is accurate enough for the algorithm to converge. The developed algorithm is based on an earlier publication on the subject, however the mathematical description, and deduction, of it is taken one step further than in this publication. The iterative algorithm used performs well under the assumption that the data used fulfills some basic criteria. These demands comprises: placement of camera, template size as well as how the parameters may vary between two observations. The iterative algorithm is also potentially faster than exhaustive search methods, because few iterations are needed when the parameters change slowly. Better initial parameters should improve stability and speed of convergation. Other suggestions that could give better performance is discussed, e.g., methods to better extract the target from the surroundings.