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UBC Theses and Dissertations

Team LSTM : player trajectory prediction in basketball games using graph-based LSTM networks Cohan, Setareh


Autonomous systems deployed in human environments must have the ability to understand and anticipate the motion and behavior of dynamic targets. More specifically, predicting the future positions of agents and planning future actions based on these predictions is a key component of such systems. This is a challenging task because the motion behavior of each agent not only depends on its own goal intent, but also the presence and actions of surrounding agents, social relations between agents, social rules and conventions, and the environment characteristics such as topology and geometry. We are specially interested in the problem of human motion trajectory prediction in real-world, social environments where potential interactions affect the way people move. One such environment is a basketball game with dynamic and complex movements driven by various social interactions. In this work, we focus on player motion trajectory prediction in real basketball games. We view the problem of trajectory prediction as a sequence prediction task where our goal is to predict the future positions of players using their past positions. Following the success of recurrent neural network models for sequence prediction tasks, we investigate the ability of these models to predict motion trajectories of players. More specifically, we propose a graph-based pooling procedure that uses relation networks and incorporates it with long short-term memory networks. We study the effect of different graph structures on the accuracy of predictions. We evaluate the different variations of our model on three datasets; two publicly available pedestrian datasets of ETH and UCY, as well as a real-world basketball dataset. Our model outperforms vanilla LSTM and Social-LSTM baselines on both of these datasets.

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Attribution-NonCommercial-NoDerivatives 4.0 International