Research on the Tracking of Objects in Image Based on Atomic Clustering Based Dictionary Learning

In view of the deficiency of detail and texture structure information loss in image tracking process, an image tracking algorithm based on dictionary learning and atomic clusters is proposed in this paper. First of all, use noised images to obtain adaptive redundant dictionary through dictionary learning algorithm, then extract HOG features and gray statistical characteristics of each atom in dictionary with this algorithm to form feature sets which is used for classifying atoms in redundant dictionary into two types (including de-noised and noised atoms), and finally recover images with the help of de-noised atoms, achieving the goal of denoising. Experiments show that with this method, target tracking errors can be effectively reduced, thus accurately identifying target location and greatly improving the accuracy of target tracking, when the target has violent changes in angle and background.