# Object and Event Detection Pipeline for Rink Hockey Games
###### Abstract  
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm's performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7,insights into opponent behavior. By analyzing the data collected through deep learning techniques, coaches can make informed decisions and develop customized training programs to enhance their players' performance. Moreover, the application of deep learning-based game visualization has the potential to transform the role of referees in rink hockey. In this context, referees can play a pivotal role in ensuring fair play and enforcing the rules of the game. However, due to the fast-paced nature of rink hockey, it can be challenging for referees to keep up with all the action and make accurate decisions in real time. The incorporation of deep learning techniques enables referees to receive real-time assistance through computer vision systems. Such systems are capable of tracking players, identifying fouks or infractions, and even providing instant feedback to referees. This reduces the potential for human error and improves the overall fairness of the game. In addition to their application in the field of game visualization and referee work, these automatic techniques can also significantly impact player training in rink hockey. Training in this sport necessitates the acquisition of a combination of technical abilities, physical conditioning, and tactical comprehension. The utilization of deep learning algorithms enables the creation of personalized and optimized training programs, which are designed to meet the specific needs of individual players. The data collected from game visualization can be utilized to identify strengths and weaknesses, monitor progress, and provide targeted feedback to players. This information can be used by coaches to design training sessions that address specific areas for improvement and enhance overall performance. Furthermore, the application of deep learning techniques can facilitate the identification of potential injury risks through the analysis of player movements. By monitoring the performance data of players, coaches and medical staff are able to identify signs of fatigue, monitor load management, and make informed decisions to minimize the risk of injuries. This paper proposes a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Given the limited availability of developed content relating to this sport, we have decided to create a dataset and test the effectiveness of deep learning and computer vision improvements in the real world. Consequently, object detection, specifically employing the YOLOv7 algorithm, can be used to track the movement of the hockey ball and players on the rink, providing coaches, players, and fans with valuable information. Our paper also examines the advantages of using this approach for object and event detection in rink hockey, as well as its potential impact on the sport itself. The remainder of the paper is organized as follows. Section 2 presents a discussion of related work.