# Object and Event Detection Pipeline for Rink Hockey Games
## 2 Computer Vision Applied to Hockey Sports  
Rink hockey and ice hockey are two team sports that share numerous similarities, yet there are also notable differences between them. One of the most significant distinctions between the two sports is the type of equipment employed. Rink hockey is typically played on a dry surface, such as a concrete rink or a sports court, and the playing area is rectangular (Figure 1 right). In contrast, ice hockey is played on an ice rink, which is typically oval in shape (Figure 1 left). The field material is a significant factor influencing the variations in movement patterns and techniques employed by players, as they must adapt to different surfaces and levels of friction and stability. The participants utilize sticks to propel a hard ball/puck into the opposing team's goal, as the game is played on a hard, smooth surface, such as a basketball court or roller Fink. The faster pace of play in roller hockey is a consequence of the players' greater mobility on roller skates. Furthermore, the regulations of the two sports diverge in certain respects. For example, in standard conditions on the playing rink, each team in rink hockey is composed of one goalkeeper and four field players. In ice hockey, each team typically comprises six players. Furthermore, the offside rule is frequently employed in ice hockey, in contrast to rink hockey. The latest innovations in object detection techniques have resulted in considerable increases in both the accuracy and speed of object detection [1]. Convolutional neural networks (CNNs) represent one of the most extensively used techniques for object detection, having obtained state-of-the-art results on diverse object detection benchmarks [2]. One of the primary challenges in rink hockey object detection is the substantial diversity in the appearance of objects due to factors such as lighting conditions, player uniforms, and background noise. Researchers have proposed a number of approaches to address this challenge, including the use of multiple CNNs with distinct architectures [3] and the use of data augmentation techniques to increase the diversity of the training data [4]. In rink hockey, these techniques can be employed for a multitude of purposes, including player tracking, ball tracking, and game analysis. Unfortunately, to date, there has been little work done on object detection in rink hockey. This encompasses both the generation of a dataset and the implementation of deep learning techniques for the associated predictions. However, in relation to ice hockey, there is a plethora of content available, including tracking and identification of players, characteristics of the playing field, and match analysis [5; 6; 7]. In [5], a system for automatically tracking and identifying players in NHL streamed videos was proposed. The system comprised three components: player tracking, team identification, and player identification. The authors tested five state-of-the-art tracking algorithms on the hockey player tracking dataset [8; 9; 10; 11; 12]. The most effective tracking performance was achieved by utilizing the MOT Neural Solver tracking model [10], which had been re-trained on the hockey dataset with a Multi-Object Tracking Accuracy (MOTA) score of 94.5%. Furthermore, for the purpose of identifying teams, the away team jerseys were grouped into a single class, whereas the home team jerseys were grouped according to their color. In the case of the team identification dataset, a CNN was trained, achieving an accuracy of 97% on the test set. Furthermore, a novel player identification model was presented that employed a temporal one-dimensional convolutional network to identify players from sequences of player bounding boxes. Utilizing the available National Hockey League (NHL) game roster data, the player identification model achieved an accuracy of 83% in player identification. In [6], the authors developed a cascaded convolutional neural network (CNN) model comprising two phases for the detection of ice hockey players.