Time-series Imputation of Temporally-occluded Multiagent Trajectories
This addresses the challenge of sporadic observations in multiagent systems, like sports analytics, but is incremental as it builds on existing graph and variational autoencoder techniques.
The paper tackles the problem of imputing missing observations in multiagent trajectories, such as off-screen players in football, by using past and future data to estimate unobserved states, and demonstrates that their Graph Imputer method outperforms state-of-the-art approaches, including those specifically designed for football.
In multiagent environments, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents' decision-making processes, make such systems complex and interesting to study from a dynamical perspective. Significant research has been conducted on learning models for forward-direction estimation of agent behaviors, for example, pedestrian predictions used for collision-avoidance in self-driving cars. However, in many settings, only sporadic observations of agents may be available in a given trajectory sequence. For instance, in football, subsets of players may come in and out of view of broadcast video footage, while unobserved players continue to interact off-screen. In this paper, we study the problem of multiagent time-series imputation, where available past and future observations of subsets of agents are used to estimate missing observations for other agents. Our approach, called the Graph Imputer, uses forward- and backward-information in combination with graph networks and variational autoencoders to enable learning of a distribution of imputed trajectories. We evaluate our approach on a dataset of football matches, using a projective camera module to train and evaluate our model for the off-screen player state estimation setting. We illustrate that our method outperforms several state-of-the-art approaches, including those hand-crafted for football.