A New Algorithm for Tracking Objects in Videos of Cluttered Scenes

The work presented in this paper describes a novel algorithm for automatic video object tracking based on a process of subtraction of successive frames, where the prediction of the direction of movement of the object being tracked is carried out by analyzing the changing areas generated as result of the object’s motion, specifically in regions of interest defined inside the object being tracked in both the current and the next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of the object being tracked in the next frame using a function which measures the grade of dissimilarity between the region of interest defined inside the object being tracked in the current frame and a moving region in a next frame. This moving region is displaced in the direction of the object’s motion predicted on the process of subtraction of successive frames. Finally, the location of the moving region of interest in the next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of the object being tracked in the next frame. On the other hand, it is also designed a testing platform which is used to create virtual scenarios that allow us to assess the performance of the proposed algorithm. These virtual scenarios are exposed to heavily cluttered conditions where areas which surround the object being tracked present a high variability. The results obtained with the proposed algorithm show that the tracking process was successfully carried out in a set of virtual scenarios under different challenging conditions.