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Cricket Shot Simple CNN in Python Projects
र 1000
Cricket Shot Simple CNN in Python Projects
Technical Details
Domain : Python
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
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About This Product
Cricket Shot Simple CNN in Python Projects
Abstract
Cricket shot recognition is an emerging application in sports analytics, useful for coaching, performance tracking, and automated video analysis. Identifying different cricket shots such as cover drive, pull shot, sweep, or straight drive requires analyzing spatial features from video frames. This project, Cricket Shot Classification Using Simple Convolutional Neural Network (CNN) in Python, builds a deep learning model to classify cricket shots based on image/video input. Using Python libraries like TensorFlow/Keras, OpenCV, NumPy, and Matplotlib, the system processes cricket video frames, extracts features, and applies a CNN model for classification. The project provides a lightweight but effective approach for cricket analytics, enabling automated highlight generation, coaching insights, and shot statistics analysis.
Existing System
Traditional cricket video analysis depends heavily on manual annotation by experts, rule-based feature extraction, or basic machine learning algorithms that often rely on handcrafted features. These approaches are time-consuming, prone to human error, and not scalable for large datasets. Moreover, conventional systems cannot adapt to variations in camera angles, lighting, or player movements, limiting their accuracy in recognizing different cricket shots.
Proposed System
The proposed system introduces a Simple CNN-based cricket shot classification model. The workflow includes data collection (frames extracted from cricket videos), data preprocessing (resizing, normalization, and augmentation), and training a Convolutional Neural Network with layers such as convolution, pooling, flattening, and fully connected layers. The CNN model automatically learns spatial features of player movements and bat-ball interactions, reducing dependency on manual feature engineering. Compared to existing methods, this approach provides faster, scalable, and more accurate classification of cricket shots, making it suitable for sports analytics platforms and automated commentary systems.
Abstract
Cricket shot recognition is an emerging application in sports analytics, useful for coaching, performance tracking, and automated video analysis. Identifying different cricket shots such as cover drive, pull shot, sweep, or straight drive requires analyzing spatial features from video frames. This project, Cricket Shot Classification Using Simple Convolutional Neural Network (CNN) in Python, builds a deep learning model to classify cricket shots based on image/video input. Using Python libraries like TensorFlow/Keras, OpenCV, NumPy, and Matplotlib, the system processes cricket video frames, extracts features, and applies a CNN model for classification. The project provides a lightweight but effective approach for cricket analytics, enabling automated highlight generation, coaching insights, and shot statistics analysis.
Existing System
Traditional cricket video analysis depends heavily on manual annotation by experts, rule-based feature extraction, or basic machine learning algorithms that often rely on handcrafted features. These approaches are time-consuming, prone to human error, and not scalable for large datasets. Moreover, conventional systems cannot adapt to variations in camera angles, lighting, or player movements, limiting their accuracy in recognizing different cricket shots.
Proposed System
The proposed system introduces a Simple CNN-based cricket shot classification model. The workflow includes data collection (frames extracted from cricket videos), data preprocessing (resizing, normalization, and augmentation), and training a Convolutional Neural Network with layers such as convolution, pooling, flattening, and fully connected layers. The CNN model automatically learns spatial features of player movements and bat-ball interactions, reducing dependency on manual feature engineering. Compared to existing methods, this approach provides faster, scalable, and more accurate classification of cricket shots, making it suitable for sports analytics platforms and automated commentary systems.
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