Nonlinear climbing video indexing

Human Pose Estimation is one of the most promising research areas in the field that links together Artificial Intelligence and Computer Vision. Its applications are countless since it mimics one of the principal human capabilities: understanding the physical space that surrounds us, and the interaction of the context with people. Sport is the field that can most benefit from this technology since it involves dynamic movements of individuals. I present in this project the research and the development of a fully-working application that exploits human pose estimation to perform nonlinear queries to retrieve the exact pose and movements that the user is looking for. I applied this framework to climbing, in which the training phase involves mostly figure out how to perform a certain pose by looking at how other climbers have done it.