Deep Learning Algorithm-Based Target Detection and Fine Localization of Technical Features in Basketball

Abstract

Based on SSD to detect players, a super-pixel-based FCN-CNN player segmentation algorithm is proposed to filter out the complex background around players, which is more conducive to the subsequent pose estimation for target detection and fine localization of basketball technical features. The high resolution capability of CNN is used to extract images and perform computational preprocessing to identify typical basketball sports actions in video streams—rebounds, shots, and passes—with an accuracy rate of up to 95.6%. By comparing with three classical classification algorithms, the results prove that the target detection system proposed in this study is effective for target detection and fine localization of basketball sports technical features.