ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-06082021-100521


Tipo di tesi
Tesi di laurea magistrale
Autore
FRANCESCHELLI, LUIGI
URN
etd-06082021-100521
Titolo
Machine Learning gesture detection from pose recognition in video analysis
Dipartimento
INFORMATICA
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Relatori
relatore Prof. Prencipe, Giuseppe
correlatore Dott. Tommasi, Alessandro
correlatore Dott. Zavattari, Cesare
Parole chiave
  • video analysis
  • gesture detection
  • sport analytics
  • deep learning
  • poses recognition
  • machine learning
  • data mining
Data inizio appello
25/06/2021
Consultabilità
Non consultabile
Data di rilascio
25/06/2091
Riassunto
Recent developments in deep learning and pose recognition make more accessible tracking objects in video sequences. We believe that a useful application of pose recognition system could be in sport analysis field, in particular in the detection of athletes' gestures. In this work we use a human poses dataset of tennis video sequences and we apply machine learning models to detect when a shot is made in the videos. Particularly we build a frame by frame human poses dataset of tennis games video sequences, using the most advanced human poses recognition tools. We label the shot frames, those where the player hits the ball with the racket. Then we apply specific normalization to center each frame compared to the player's body. Next we test several criteria to aggregate consecutive frames into groups, and in each group we compute distance based features to capture the movements of the player and their direction. For each combination, we apply several machine learning models that try to predict in every of those groups whether a shot is present. Results show us that boosting models are able to accurately capture the shot sequences in a video and to separate them from those where there is not a shot.
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