Multiple human tracking system for unpredictable trajectories

Tracking multiple objects into a scene is one of the most active research topics in computer vision. The art of identifying each target within the scene along a video sequence has multiple issues to be solved, being collision and occlusion events among the most challenging ones. Because of this, when dealing with human detection, it is often very difficult to obtain a full body image, which introduces complexity in the process. The task becomes even more difficult when dealing with unpredictable trajectories, like in sport environments. Thus, head-shoulder omega shape becomes a powerful tool to perform the human detection. Most of the contributions to this field involve a detection technique followed by a tracking system based on the omega-shape features. Based on these works, we present a novel methodology for providing a full tracking system. Different techniques are combined to both detect, track and recover target identifications under unpredictable trajectories, such as sport events. Experimental results into challenging sport scenes show the performance and accuracy of this technique. Also, the system speed opens the door for obtaining a real-time system using GPU programing in standard desktop machines, being able to be used in higher-level human behavioral systems, with multiple applications.