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Classification of puck possession events in ice hockey

University of British Columbia

Group activity recognition in sports is often challenging due to the complex dynamics and interaction among the players. In this thesis, we propose a deep architecture to classify puck possession events in ice hockey. Our model consists of three distinct phases: feature extraction, feature aggregation and, learning and inference. For the feature extraction and aggregation, we use a Convolutional Neural Network (CNN) followed by a late fusion model on top to extract and aggregate different types of features that includes handcrafted homography features for encoding the camera information. The output from the CNN is then passed into a Recurrent Neural Network (RNN) for the temporal extension and classification of the events. The proposed model captures the context information from the frame features as well as the homography features. The individual attributes of the players and the interaction among them is also incorporated using a pre-trained model and team pooling. Our model requires only the player positions on the image and the homography matrix and does not need any explicit annotations for the individual actions or player trajectories, greatly simplifying the input of the system. We evaluate our model on a new Ice Hockey Dataset and a Volleyball Dataset. Experimental results show that our model produces promising results on both these challenging datasets with much simpler inputs compared with the previous work.

Text

2017-09-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63149

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/