Attention and Multi-Scale Fusion for Shadow Removal of Embedded Basketball Players

Player shadows in basketball games show complexity and diversity due to various factors such as lighting conditions, player movements, camera positions, etc. In the shadow removal task, the attention mechanism aids the model in focusing on the key areas in the image, that is, the players themselves and the shadows around them, and may ignore some details, resulting in poor effects in the shadow removal of embedded basketball game players. To this end, a shadow removal method for embedded basketball game players with design attention and multi-scale fusion is proposed. By extracting the texture of the shadow image area of the local embedded basketball player, setting the gray value threshold of the center point of the image area window, obtaining the Local Binary Patterns (LBP) code of the local texture feature structure of the image, scanning the LBP code of the shadow image of the embedded basketball player, and obtaining the final characteristic parameters of the shadow image of the embedded basketball player, the pixels of the shadow image of the embedded basketball player are balanced and adjusted, and the size of the image is determined by the slope of the transformation function, and the shadow image of the embedded basketball player is within the normal proportion range. The consistency of multi-scale detail pixels is adjusted to achieve pixel segmentation of shadow images of embedded basketball players. Analyzing the basic principles of the attention mechanism, the shadow images of embedded basketball players are collected for key-value pairs to represent action information, and a key-value set is set. Encoding is carried out by the encoder in the attention mechanism, and the dependence of the foul action after encoding is calculated and decoded to determine the shadow image of the embedded basketball player to achieve shadow recognition by obtaining the shadow pixel point chromaticity space of the embedded basketball player color vector value, we determine the chromaticity similarity difference, compare the chromaticity similarity difference with the threshold, design a multi-scale fusion embedded basketball player shadow removal model, and implement the research. Experimental results show that the proposed method can effectively remove the shadow of embedded basketball players.