  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.
