In this paper, we propose a new generative model for multi-agent trajectory data, focusing
on the case of multi-player sports games. Our model leverages graph neural networks …

Many real-world video analysis applications require the ability to identify domain-specific
events in video, such as interviews and commercials in TV news broadcasts, or action …

Understanding the principles of real-world biological multi-agent behaviors is a current
challenge in various scientific and engineering fields. The rules regarding the real-world …

Trajectory forecasting and imputation are pivotal steps towards understanding the
movement of human and objects, which are quite challenging since the future trajectories …

Predicting outcomes in sports is important for teams, leagues, bettors, media, and fans.
Given the growing amount of player tracking data, sports analytics models are increasingly …

We study the automatic annotation of situations in soccer games. At first sight, this translates
nicely into a standard supervised learning problem. However, in a fully supervised setting …

Spatiotemporal datasets based on player tracking are widely used in sports analytics
research. Common research tasks often require the analysis of game events, such as …

Understanding trajectories in multi-agent scenarios requires addressing various tasks,
including predicting future movements, imputing missing observations, inferring the status of …

The aims of the present study were to systematize a set of concatenated space creation
dynamics in basketball and to use it to analyze teams' offenses. Space creation dynamics …

Massive datasets of spatial trajectories representing the mobility of a diversity of moving
objects are ubiquitous in research and industry. Similarity search of a large collection of …