**Abstract**

This chapter deals with the application of deep learning methods in sports scenes for the purpose of detecting and tracking the athletes and recognizing their activities. The scenes recorded during handball games and training activities will be used as an example. Handball is a team sport played with the ball with well-defined goals and rules, with a given number of players who can participate in the game as well as their roles. Athletes move quickly throughout the field during the game, change position and roles from defensive to offensive, use different techniques and actions, and very often are partially or completely occluded by another athlete. If artificial lighting and cluttered background are additionally taken into account, it is clear that these are very challenging tasks for object detectors and trackers. The chapter will present the results of various experiments that include player and ball detection using state-of-the-art deep convolutional neural networks such as YOLO v3 or Mask R-CNN, player tracking using Deep Sort, key player determination using activity measures, and action recognition using LSTM. In the conclusion, open issues and challenges in applying deep learning methods in such a dynamic sports environment will be discussed.

**Keywords:** handball, object detector, object tracking, action recognition, person detection, deep convolutional neural networks, YOLO, mask R-CNN, LSTM, DeepSort, Hungarian algorithm, optical flow, STIPs
