Real-Time Tracking of Basketball Trajectory Based on the Associative MCMC Model

In basketball videos, the trajectories of a basketball changes rapidly. Since the visual features changes in a more homogeneous region, the frame difference method is a suitable basis for trajectory real-time tracking. However, traditional methods need a huge number of iterative calculations in a random image to find spatial feature differences to segment the basketball from to frame, resulting in tracking lag. Therefore, a real-time tracking method of basketball trajectory is designed based on an associative Markov Chain Monte Carlo (MCMC) model. From pixel illumination differences between two adjacent frames in basketball game videos, the basketball’s movement is determined, and the foreground and background of the basketball frame are separated. Then, coordinates of the basketball are detected by a Convolutional Neural Network (CNN), and the change of coordinates is used to construct a visual 2D mapping model, which calculates both angular and linear acceleration of the basketball. To solve the interaction problem of randomness and spatial variability, an associative MCMC model is designed to segment basketball images with simple conditions, and a Bayesian network is established to input parameters of the segmented basketball movement for the determination of trajectory deviation. Finally, basketball movement trends are calculated to achieve real-time tracking of the trajectory in the basketball video. The experimental results show that compared with the original running path, this method has the smallest difference in tracking trajectory error, and the estimation error does not exceed 0.2 when the false alarm rate is 100. The trajectory tracking time is always less than 2.2 seconds, indicating that it has good trajectory tracking ability.