ICTACT Journal on Image and Video Processing

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Object Detection in Hockey Sport Video via Pretrained YOLOV3 Based Deep Learning Model


Affiliations
1 Department of Electronics and Communication Engineering, Gujarat Technological University, India., India
2 Department of Electronics and Communication Engineering, V.V.P. Engineering College, India., India
     

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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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  • Object Detection in Hockey Sport Video via Pretrained YOLOV3 Based Deep Learning Model

Abstract Views: 95  |  PDF Views: 0

Authors

Suhas H. Patel
Department of Electronics and Communication Engineering, Gujarat Technological University, India., India
Dipesh Kamdar
Department of Electronics and Communication Engineering, V.V.P. Engineering College, India., India

Abstract


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.

References