Analysis of Movement and Activities of Handball Players Using Deep Neural Networks.

This paper focuses on image and video content analysis of handball scenes and applying deep learning methods for detecting and tracking the players and recognizing their activities. Handball is a team sport of two teams played indoors with the ball with well-defined goals and rules. The game is dynamic, with fourteen players moving quickly throughout the field in different directions, changing positions and roles from defensive to offensive, and performing different techniques and actions.

Active Player Detection in Handball Scenes Based on Activity Measures.

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player.