Time-series Imputation of Temporally-occluded Multiagent Trajectories

• Main Authors: Omidshafiei, Shayegan, Hennes, Daniel, Garnelo, Marta, Tarassov, Eugene, Wang, Zhe, Elie, Romuald, Connor, Jerome T, Muller, Paul, Graham, Ian, Spearman, William, Tuyls, Karl
• Format: Journal Article
• Language: English
• Published: 08.06.2021
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Learning; Computer Science - Multiagent Systems
• DOI: 10.48550/arxiv.2106.04219

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.