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Nonlinear climbing video indexing

Daniele Pace

Nonlinear climbing video indexing.

Rel. Bartolomeo Montrucchio. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021

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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.

Relators: Bartolomeo Montrucchio
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 77
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: AALTO UNIVERSITY OF TECHNOLOGY - School of Science (FINLANDIA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/19206
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