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Related Concept Videos

Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation.

Yousef I Mohamad1,2, Samah S Baraheem1, Tam V Nguyen1

  • 1Department of Computer Science, University of Dayton, Dayton, OH 45469, USA.

Journal of Imaging
|August 30, 2021
PubMed
Summary

This study enhances sports event recognition in Olympic images using deep learning. ResNet-50 with photobombing data augmentation achieved 90% accuracy, improving computer vision applications.

Keywords:
data augmentationdeep learningimage classificationsport event classificationtransfer learning

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • The proliferation of sports data necessitates efficient event recognition systems.
  • Applications include sports search, data analysis, healthcare monitoring, and surveillance.

Purpose of the Study:

  • To evaluate deep learning models for recognizing Olympic sport events.
  • To develop an accurate and efficient automatic event recognition system.

Main Methods:

  • A dataset (Olympic Games Event Image Dataset - OGED) of 10 Olympic events was created.
  • Transfer learning was applied to AlexNet, VGG-16, and ResNet-50 architectures.
  • Data augmentation techniques, including photobombing guided augmentation, were utilized.

Main Results:

  • ResNet-50 model demonstrated superior performance.
  • The proposed photobombing guided data augmentation significantly improved accuracy.
  • Achieved 90% accuracy in recognizing Olympic sport events.

Conclusions:

  • Deep learning models, particularly ResNet-50, are effective for sports event recognition.
  • Photobombing guided data augmentation is a valuable technique for enhancing model performance.
  • The developed system offers a promising solution for sports image and video analysis.