Abnormal activities identification using Deep Q Network from IoT Surveillance Systems

With the rapid proliferation of Internet of Things (IoT) devices, surveillance systems have become ubiquitous, providing crucial monitoring capabilities across various domains such as security, healthcare, and manufacturing. However, efficiently detecting abnormal activities within the vast streams of surveillance data remains a significant challenge. Traditional methods often struggle to adapt to dynamic environments and diverse anomalies, necessitating more sophisticated approaches. This paper proposes a novel framework for abnormal activity identification in IoT surveillance systems leveraging Deep Q Network (DQN) architecture. DQN, a form of reinforcement learning, has shown remarkable success in learning optimal strategies from high-dimensional sensory inputs. By integrating DQN into IoT surveillance systems, we aim to enhance their anomaly detection capabilities.