Object Detection and Tracking Based on Trajectory in Broadcast Tennis Video

Abstract

Ball, player detection and tracking in Broadcast Tennis Video (BTV) is a challenging task in tennis video semantic analysis. Informally, the challenges are due to the camera motion and the other causes such as the small size of the tennis ball and many objects resembles like ball, while the player, the human body along with the tennis racket is not detected completely. In this paper proposed an improved object tracking technique in BTV. In order to track the ball, logical AND operation is applied between the created background and image difference is performed, from that the ball candidates are detected by applying threshold values and dilated. Finally the ball is tracked. Player detection is performed from AND results by finding the biggest blob and filling the whole detected object by removing the small one and the players are tracked based on the contour. The experimental result shows the proposed approach achieved the higher accuracy in object identification, and their tracking. It is achieved a high hit rate and less fail rate for ball tracking while for player tracking is measured by Multiple Object Tracking Precision (MOTP).