Lopes, J.M.; Mota, L.P.; Mota, S.M.; Torres, J.M.; Moreira, R.S.; Soares, C.; Pereira, I.; Gouveia, F.; Sobral, P. Object and Event Detection Pipeline for Rink Hockey Games. Preprints2024, 2024040383. https://doi.org/10.20944/preprints202404.0383.v1
APA Style
Lopes, J.M., Mota, L.P., Mota, S.M., Torres, J.M., Moreira, R.S., Soares, C., Pereira, I., Gouveia, F., & Sobral, P. (2024). Object and Event Detection Pipeline for Rink Hockey Games. Preprints. https://doi.org/10.20944/preprints202404.0383.v1
Chicago/Turabian Style
Lopes, J.M., Feliz Gouveia and Pedro Sobral. 2024 "Object and Event Detection Pipeline for Rink Hockey Games" Preprints. https://doi.org/10.20944/preprints202404.0383.v1
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 quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.
Keywords
Deep Learning; Rink Hockey; Objects and Events Detection; Artificial Intelligence; Computer Vision; YOLOv7; Team Sports
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
