Tracking athletic performance in basketball can require significant time and resources, but the resulting information can be extremely valuable for a developing player and their coach. Fortunately, advancements in computer vision can provide an opportunity to automatically track shooting performance. Further, this information can be provided to the players in an entertaining and fun gaming atmosphere to encourage player development. The objective of this project is to advance deep learning algorithms to provide real-time biomechanical feedback that can be used to develop training and entertainment software for youth basketball players. This exciting work by Pipeline Studios Ltd., in collaboration with McMaster University, involves the use of deep learning-based approaches to track shooting mechanics and performance. Significant computational resources are initially required to test the limits of these deep learning models for image classification, object detection, event detection, and human pose estimation. The advancement of these algorithms on large computational networks will be required for the training and entertainment software which can support basketball athlete development in Canada.

Industry Partner(s):Pipeline Studios INC.

Academic Institution:McMaster University

Academic Researcher: Dylan Kobsar

Focus Areas: Health

Platforms: GPU