## 2 Related Works
### Athletic Recognition and Tracking
You Only Look Once (YOLO) is an algorithm first proposed by [PERSON] in 2016. It is a real-time object detection framework that processes an entire image in a single neural network pass to predict bounding boxes and class probabilities in a single evaluation. By framing object detection as a global optimization problem, YOLO minimizes background errors and combines feature extraction, bounding box prediction, and classification into a unified pipeline. This makes it highly suitable for real-time applications in various domains, and as of now, it has been released up to version 11. In this work, we use YOLO, which integrates techniques such as dynamic anchor assignment and adaptive task prioritization to improve its architecture, enhance accuracy, and achieve faster inference speeds. [PERSON] et al. launched a study detecting players and the ball in a handball sports game using the YOLOv2 model [17]. The ball only takes a few pixels in an image, which makes it complicated to detect, and it can shift its shape according to motion blurs. Furthermore, detecting handball players is challenging due to their positions in front of the camera, often hidden behind each other, and it is important to detect who is carrying the ball currently. This study applied indoor/outdoor game play data of handball and the COCO dataset to prevent overfitting and generalization. The model mainly focused on detecting the ball and players instead of the whole view, and outperformed other models by values from 0.94% to 7.34%. [PERSON], [PERSON] et al. suggested a method to detect and track basketball players with the combination of YOLO and a Deep Fuzzy LSTM Network [18]. Detecting players in a basketball game is challenging with many constraints like calibration from various cameras, occlusion from on-court and off-court cameras, and inconsistent lights. This study applied YOLO to track players in each frame and applied LSTM and Fuzzy logic to distinguish each player's actions. Fuzzy Logic supports LSTM to overcome its limits to make a precise prediction system. The suggested model was validated on the dataset SpaceJam and the Basketball-51 dataset. Even though YOLO models have brought about huge development for ball tracking, there are still some challenging issues like occlusion and 3D tracking. Especially in the sports field, the environment is uncontrolled and there is some domain knowledge required for the model to detect human activities during the game. Complicated backgrounds and inconsistent lights, detecting players moving at a fast tempo, and distinguishing players and referees based on prior information about the team are also some reasons that make this task challenging. Moreover, detecting the ball and players in sports is also required but challenging due to the blurry pixels of the ball in a frame. To tackle these challenges, our study focuses on developing a robust algorithm for detecting and tracking players, referees, and balls in a sports game video with an uncontrolled dataset. In particular, our study suggests a method to optimize sports commentary generation by fine-tuning existing Yolo models, demonstrating the feasibility of achieving high-quality results. For object tracking, we propose the following algorithm. In each frame, the YOLO detection model is used to identify the bounding boxes of players. Subsequently, the Intersection over Union (IoU) is calculated between the bounding boxes from the current frame and those from the previous five frames. The bounding boxes with the highest IoU are then linked together to track the movement paths of the players. Additionally, this study incorporates domain knowledge specific to basketball to distinguish between teams, players, and referees while also recognizing in-bounds and out-of-bounds events.