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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network

Anik Sen1,2, Kaushik Deb1, Pranab Kumar Dhar1

  • 1Department of Computer Science & Engineering, Chittagong University of Engineering & Technology (CUET), Chattogram 4349, Bangladesh.

Sensors (Basel, Switzerland)
|April 30, 2021
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Summary

This study introduces a hybrid deep learning model for classifying cricket batting shots. The VGG16-GRU model achieved 93% accuracy, improving cricket analysis and coaching tools.

Keywords:
batting shotsconvolutional neural networkdeep learninggated recurrent unittransfer learning

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Sports Analytics

Background:

  • Automated recognition of cricket batting shots is valuable for applications like advertising, commentary, and coaching.
  • Manual feature extraction from video is challenging due to shot similarities and varying conditions.

Purpose of the Study:

  • To propose a hybrid deep neural network architecture for classifying 10 different cricket batting shots.
  • To develop a novel dataset (CricShot10) suitable for this classification task.

Main Methods:

  • Utilized a hybrid deep learning approach combining Convolutional Neural Network (CNN) for feature extraction and Gated Recurrent Unit (GRU) for temporal dependencies.
  • Investigated various CNN architectures, including conventional, dilated, and transfer learning models (VGG16, InceptionV3, Xception, DenseNet169).
  • Developed refined VGG16-GRU models by selectively unfreezing layers for improved performance.

Main Results:

  • The initial VGG16-GRU model achieved 86% accuracy.
  • Further optimization by unfreezing final layers of VGG16 resulted in 93% accuracy on the CricShot10 dataset.
  • The proposed hybrid architecture demonstrated superior performance compared to other evaluated methods.

Conclusions:

  • The hybrid deep neural network architecture, particularly VGG16-GRU with fine-tuning, is highly effective for classifying cricket batting shots.
  • The CricShot10 dataset presents a robust benchmark for evaluating such models under realistic conditions.
  • This research advances automated sports analysis, offering potential for enhanced user experiences and coaching tools.