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Ball tracking technology basically alters the course of sports analytics and renders a sharply visual, data-intensive view towards the dynamics of play. For games such as football, tennis, cricket, basketball, and squash, wherein the movement of the ball normally determines the flow of matches and their consequent outcome, its accurate tracking against speed and interaction with players assumes fabulous value.
This paper presents a novel approach addressing the issues that lead to solid meshes that are significant in 3D modeling, with wide applications in manufacturing, product design, medical modeling, and virtual reality by converting solid meshes into hollow meshes using the Convolutional Reconstruction Model (CRM) using the CraftsMan system, leveraging pre-trained checkpoints and advanced geometric representations.
In sports analytics, accurate player tracking is paramount for extracting meaningful insights and improving game strategies.The systems of player tracking based on the bounding boxes can be deteriorated by some noise-related issues, such as flickering boxes and bad estimations of the players' position and erroneous distance measurements. These noise arise due to rapid player movement, occlusion, and detection inconsistencies, ultimately affecting the precision of the analysis.
Object detection is an important aspect of sports analytics, but occlusions, as players or objects collide, and or obstruct, significantly impact accurate detection. In this paper, we propose a new approach to enhance object detection in sports footage, by merging explicit annotation and post-processing techniques.
The demand in sectors like gaming, virtual reality, simulation, and film for complex detailed models with 3D modeling and rendering technologies has grown very high.
Object detection is an important aspect of sports analytics, but occlusions, as players or objects collide, and or obstruct, significantly impact accurate detection. In this paper, we propose a new approach to enhance object detection in sports footage, by merging explicit annotation and post-processing techniques.