Prioritizing skill action recognition of basketball using decision support system
Yunxing He · Research Square · 2022
Abstract Human action understanding is vital in many computer vision applications, including sports narrative, virtual reality games, and human computer interaction (HCI) systems. To recognize human actions, a video stream or image sequence must contain many actions. For example, modern video surveillance systems need to recognize human action to improve financial institution security. Human action recognition can help with automatic sports narrative and video captioning. It's important for HCI, notably for virtual reality games, sports storytelling, and video captioning. However, complex backgrounds, action occlusion, and fluctuating lighting conditions make human action identification difficult nowadays. Basketball high-difficulty action recognition technology is used to identify and evaluate player movement. Video recognition is a vital tool for improving basketball instruction. Technology and injury limit traditional sports target recognition, preventing the desired effect. The current study has used decision support system for prioritizing the skill action recognition of basketball. The tool of super decision was used for the process of experimental work. Results of the experiments reveal that the proposed work is effective for prioritizing skill action recognition showed satisfactory results.