Monitoring of moving object debugging, and for video images

Object tracking, which is one of the subjects of computer vision, is the problem of predicting the trajectories of moving objects in a video recording. Object tracking is used in many areas such as security systems for surveillance, medicine for tracking and recognition of the diseased cells, and in the military appllications for the construction of unmanned vehicles and smart weapons.In the literature, several methods for object tracking are discussed. In this thesis two different algorithms for object tracking are used. The first one is the mean shift algorithm, which is a non-parametric method that uses the color distribution statistics of an object. The second algorithm is a differential technique called Horn and Schunck algorithm, which is also called as optical flow algorithm that uses two image sequences for speed estimation.In this study, the methods for the solution of the problems encountered in object tracking are classified. Detailed information about the algorithms used in this thesis is given. We have developed several Matlab programs for object tracking using the aforementioned algorithms. The data obtained from these programs are compared and analyzed. And the results of these analyis are used to suggest solutions for the problems encountered during this study.