Ball Trajectory

Predicting and analyzing ball trajectories is a key area of research, driven by applications in sports analytics and robotics. Current work focuses on developing accurate trajectory models, often incorporating physical principles alongside machine learning techniques like neural networks (including convolutional and recurrent architectures) and factor graphs, to account for complex factors such as spin and impacts. These models are applied to various sports, using data from single or multiple cameras, and often integrated with other computer vision methods for player tracking and event detection. The resulting advancements improve game analysis, robotic control in dynamic environments, and the automation of sports data collection and annotation.