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Kamdar, Dipesh
- Object Detection in Hockey Sport Video via Pretrained YOLOV3 Based Deep Learning Model
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Gujarat Technological University, India., IN
2 Department of Electronics and Communication Engineering, V.V.P. Engineering College, India., IN
1 Department of Electronics and Communication Engineering, Gujarat Technological University, India., IN
2 Department of Electronics and Communication Engineering, V.V.P. Engineering College, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2893-2898Abstract
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 learningbased 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.Keywords
Sport Video Analysis, Deep Learning, YOLOv1, YOLOv2, YOLOv3, Object Detection.
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