Adaptive Segmentation of Basketball Game Video Based on Markov Random Fields

To solve the problem of low coverage of some adaptive segmentation methods for basketball game videos, this paper proposes an adaptive segmentation method for basketball game videos based on Markov random field. Obtain the average value of pixel values of all frames at a specific position from the shot, extract the keyframes of the game video, and appropriately increase or reduce the segmentation threshold to resist illumination. The background model with texture information is constructed to obtain the smooth trajectory of basketball movement. The spatial position of each point in the video background is found in different frames. After obtaining a more accurate closed edge of the target, the pixel points are filled into it, and the adaptive segmentation process is optimized based on Markov random field. Experimental results show that the proposed adaptive segmentation method achieves an average coverage of 85.50% for basketball game videos, indicating its effectiveness after introducing Markov random field.