Parallelization of a Multipartite Graph Matching Algorithm for Tracking Multiple Football Players

Villalta, Marco; Siles, F.

doi:10.1109/pdgc.2018.8745720

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Finally, the method for identifying football players using morphological. Research outlines the MPI and OpenMP-based parallel technique for a football tracking system built on multiple networks [9]. Segmentation and tracking, the two kind operations of the tracking model, are combined in the suggested model using a consumer-producer pattern, along with a send-and-receive communication pattern to spread the blob identities.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Performance and Accuracy in Real-Time Football and Player Detection Using Upgraded YOLOv5 Architecture

Zhao

2024

Int J Comput Intell Syst

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The study presents a significantly improved version of the YOLOv5 real-time object detection model for football player recognition. The proposed technique includes feature-tuning and hyper-parameter optimization methods that have been carefully selected to enhance both speed and accuracy, resulting in a superior real-time performance of the YOLOv5 architecture. Furthermore, the YOLOv5 model incorporates a SimSPPF module that enables multi-scale feature extraction with less computational power, making it a highly efficient and effective solution. We selected the GhostNet module to reduce complexity and the Slim scale detection layer for precise bounding box prediction. Our tests, conducted with recordings of multiple football matches, demonstrate that our model accurately detects both the football and players even in complex scenarios with occlusions and dynamic illumination. The suggested method outperforms the original YOLOv5n model in terms of precision, recall, and mean average precision at 0.5 IoU. It is also more computationally efficient. This method has potential applications in live broadcasting, player monitoring, and sports analytics. The upgraded YOLOv5 model demonstrates superior accuracy and efficiency compared to previous methods that rely on traditional image processing techniques or two-stage detectors. This makes it highly suitable for practical, real-world deployments.

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Section: Related Workmentioning

confidence: 99%