The YOLO, a Computer Vision Algorithms, is brought out to analyze the basketball player’s status as a dataset. It can record the players’ behavior on the court including dribbling, shooting, and running. In this way, the app could collect the field goal you made and missed. First, you should use this app to record a video of your shoot training. After that, the AI would analyze and brings out a 3d virtual diagram to interpret your performance. This diagram will show the hot zone and cold zone for your field goal. Also, the track of your ball will be displayed on the video so that you can know if the angle of your shooting is too low or too high. In the end, the AI-based on machine learning will give out a plan according to your performance on shooting. As a training mobile application supported by camera-based action recognition, the target audience is the basketball amateur players who don’t have the resources as pro players do. This project will be designed as a new training experience and will be delivered as a promo video that shows how to use the application and also the scenario people use.
Library of Congress Subject Headings
Basketball--Training--Technological innovations; Performance technology--Design; Human-computer interaction; Application software--Development
Visual Communication Design (MFA)
Department, Program, or Center
School of Design (CAD)
Zeng, Guangkun, "Camera-based deep learning AI assistant system for basketball training" (2021). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus