Deep Learning model for Anomaly Detection in Video Surveillance: A CNN Approach

Abstract

Suspicious activity encompasses a broad concept relating to actions, behaviors, or occurrences that give rise to concerns regarding potential illegality, threat, or ethical violations. This term is commonly employed in various domains such as law enforcement, cybersecurity, and financial sectors. Detecting and addressing suspicious activity often involves vigilant observation, data analysis, and the use of technology to identify patterns that deviate from established norms. Individual and community awareness is essential for recognizing and reporting such activities, contributing to the overall maintenance of safety and security. Effectively managing and responding to suspicious activity requires a combination of proactive measures, investigative tools, and collaborative efforts to prevent potential risks from escalating. With the increasing demand for robust security solutions, video surveillance systems play a crucial role in monitoring and safeguarding public spaces. This study focuses on enhancing the capabilities of video surveillance applications by employing Convolutional Neural Network (CNN) algorithms for the detection of suspicious activities. The proposed system leverages the power of deep learning to analyze video streams and identify anomalous behaviors indicative of potential threats or security breaches. The CNN algorithm is trained on a diverse dataset to learn and recognize patterns associated with normal activities as well as those considered suspicious. The model's ability to discern complex spatial and temporal relationships in video frames enables it to provide accurate and timely alerts. Key aspects of the CNN algorithm include feature extraction, spatial hierarchies, and temporal dependencies, enabling the system to discern subtle nuances in human behavior that may go unnoticed by traditional surveillance methods. The model is designed to adapt to dynamic environments and varying lighting conditions, ensuring robust performance in real-world scenarios. In the evaluation phase, the proposed system demonstrates promising results in terms of accuracy, precision, and recall, outperforming conventional video surveillance methods.