Deep Learning Based Human Violence Detection with Integrated Alarm System

Violence refers to the intentional use of physical force, power, or coercion against oneself, another person, or a group, resulting in harm, injury, or deprivation. Public safety represents a multifaceted and intricate challenge, demanding swift identification and prevention of violent incidents. Violence detection is a critical aspect of maintaining public safety and security in various domains, including surveillance, law enforcement, and online content moderation. This paper proposes a holistic approach to enhancing public safety through the advanced analysis of Closed-Circuit Television (CCTV) footage. The envisioned framework presents a system acting as an electronic guardian, tirelessly surveilling environments and promptly identifying potential violence through digital technologies. By integrating Internet of Things (IoT) devices and alarm sensors, this system operates as a vigilant observer, capable of detecting signs of aggression and stress even in dynamic or chaotic settings. Through the application of deep learning techniques, the system endeavors to replicate human observation, issuing alarms swiftly to alert security personnel. By harnessing the power of IoT and deep learning, this approach represents a paradigm shift in public safety, offering a proactive and responsive solution to the challenges posed by emerging threats. Ultimately, the proposed framework establishes a symbiotic relationship between technological innovation and public safety, paving the way for safer and more secure communities.