Detecting the Unseen: A Study of CNNs for Real Time Abnormal Event Detection

Abnormal event detection is a critical task in various domains, including surveillance, healthcare, and industrial monitoring. This paper explores the application of Convolutional Neural Network (CNNs) for detecting abnormal events in dynamic environments. By leveraging CNNs’ ability to extract spatial and temporal features, we primarily aim to enhance the accuracy and efficiency of anomaly detection. To validate our approach, we employed a comparative analysis of supervised and unsupervised learning techniques. Extensive experimentation on our datasets demonstrated that CNNs consistently outperformed traditional methods in identifying anomalies with higher precision and recall rates. The results highlight the potential of CNNs as a robust solution for abnormal event detection, effectively balancing computational efficiency and detection accuracy. Our findings highlight the importance of integrating domain-specific insights and using advanced architectures to address all the challenges in anomaly detection.