Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme.

翻译：体育设施中的异常检测因具有促进节能和优化运营效率的潜力而受到广泛关注。本文研究了机器学习，特别是深度学习在体育设施异常检测中的作用。我们探讨了利用深度学习方法完成此任务所面临的挑战与前景，旨在解决传统方法的缺陷与局限性。我们的方法涉及从体育设施采集的数据中提取特征。我们通过深度前馈神经网络（DFNN）提出了问题建模公式，并引入阈值估计技术以有效识别异常。此外，我们提出了减少误报的方法，确保异常检测的可靠性与准确性。为评估方法的有效性，我们在卡塔尔大学的水上运动中心数据集上进行了实验。结果表明，基于深度学习的方案较传统技术具有显著优势，突显了其在实际应用中的潜力。采用所提方案，通常可实现94.33%的准确率和92.92%的F1分数。