vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff6a9031000000214d010001000800 Object detection is the most common task in Sports Video Analysis. This task requires accurate object detection that can handle a variety of objects of different sizes that are partially occluded, have poor lighting, or are presented in complicated surroundings. Object in field sports includes player’s team and ball detection; this is a difficult task resulting from the rapid movement of the player and speed of the object of concern. This paper proposes a pre-trained YOLOv3, deep learning-based object detection model. We have prepared a hockey dataset consisting of four main entities: Team 1 (AUS), Team 2 (BEL), Hockey Ball, and Umpire. We constructed own dataset because there are no existing field hockey datasets available. Experimental results indicate that the pre-trained YOLOV3 deep learning model generates comparative results on this dataset by modifying the hyperparameters of this pre-trained model.
Suhas H. Patel1, Dipesh Kamdar2
Gujarat Technological University, India1, V.V.P. Engineering College, India2
Sport Video Analysis, Deep Learning, YOLOv1, YOLOv2, YOLOv3, Object Detection
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 3 , Pages: 2893 - 2898 )
Date of Publication :
Feburay 2023
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244
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