Deep Learning-Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Abstract

The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.