UBC Theses and Dissertations
Group event recognition in ice hockey Tian, Sijia
Abstract
With the success of deep learning in computer vision community, most approaches for group activity recognition in sports started relying on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). However, how to model the interactions among players and the interactions between players and the scene remains a challenging problem. In order to better model these interactions, we propose two models. Our first model combines features of all players in a scene through an attention mechanism. The aggregated feature is then concatenated with the feature of the frame and passed through an RNN to generate the final prediction. In our second model, we designed a spatial grid feature and a temporal grid feature calculated from appearance features and motion features of all players in a scene, as well as their locations. We then apply CNNs to the spatial grid feature, the temporal grid feature, target frame of the scene (the frame at which the event happens), and the stack of optical flow containing the target frame separately. Results from the four streams are fused through score fusion to make the final prediction. Inputs to our models are: the target frame image, a stack of optical flow images, bounding boxes of players and coordinates of players calculated from homography matrix of the frame. We evaluated the two models on an Ice Hockey dataset, and results show that both models produced promising results. We also provide a possible solution for event detection in a more general setting.
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Attribution-NonCommercial-NoDerivatives 4.0 International