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Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm.

Ming Fu¹, Qun Zhong², Jixue Dong¹

¹School of Science, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163316, China.

Computational Intelligence and Neuroscience

|March 29, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning and clustering model for advanced sports action recognition. The model enhances athlete movement detection and training, improving recognition accuracy and speed for professional coaching.

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Area of Science:

Computer Science
Sports Science
Artificial Intelligence

Background:

Accurate sports action recognition is crucial for professional athlete training and performance analysis.
Existing methods often struggle with complex movements and high false alarm rates.

Purpose of the Study:

To develop a novel sports action recognition model using deep learning and clustering algorithms.
To enhance the accuracy, efficiency, and learning capabilities of sports movement analysis.

Main Methods:

Utilized deep learning (DL) networks for athlete movement detection in image frames.
Implemented a clustering extraction algorithm for fusing and optimizing sports movements.
Employed iterative enhancement of negative training samples using neural networks (NN) to reduce false positives.

Main Results:

The proposed clustering extraction algorithm demonstrated superior performance compared to other methods.
Achieved higher recognition rates and lower false alarm rates in simulation experiments.
Exhibited faster recognition speeds for sports actions.

Conclusions:

The developed model effectively extracts athletes' training postures through sports movement analysis.
Offers a valuable tool for professional athlete training and provides a reference for sports movement recognition systems.