Abstract: Sports activity recognition plays a crucial role in various applications, including athlete performance analysis, sports broadcasting, and injury prevention. Traditional methods for activity detection often rely on manual observation or rule- based systems, which are labor-intensive and lack scalability. In recent years, deep learning algorithms, particularly convolutional neural networks (CNNs), have emerged as promising tools for automated sports activity detection. This research paper presents a comprehensive investigation into the application of deep learning techniques for sports activity detection. We propose a CNN-based model and evaluate its performance against existing methods using standard sports activity datasets. Our results demonstrate the effectiveness of the proposed approach in accurately detecting sports activities, surpassing traditional machine learning approaches and achieving competitive performance compared to state-of-the-art models. This study contributes to the advancement of sports analytics and provides valuable insights for researchers and practitioners in the field of activity recognition.
Keywords: Sports activity detection, Deep learning, Convolutional neural networks (CNNs), Performance evaluation, Sports analytics.
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DOI:
10.17148/IJARCCE.2024.134133
