Violence Detection in Jails and Mental Asylums

Abstract

This research investigates the use of Long- term Recurrent Convolutional Networks (LRCNs) for violence detection in video surveillance systems. LRCNs combine the strengths of Convolutional Neural Networks (CNNs) for capturing spatial information and Long Short-Term Memory (LSTM) networks for modeling temporal sequences. This combination allows the system to learn complex spatiotemporal patterns in video data, improving violence detection accuracy in environments like jails and mental health facilities. The project focuses on the integration of the LRCN model with a Telegram bot for real-time alerting and response. Upon detecting violent incidents in the video streams, the LRCN model triggers alerts through the Telegram bot, providing instant notifications to relevant authorities. The Telegram bot facilitates seamless communication and coordination among stakeholders, enabling swift action to mitigate potential risks and ensure the safety of occupants within these facilities. Through rigorous experimentation and evaluation, the effectiveness and reliability of the LRCN-based violence detection system integrated with the Telegram bot are demonstrated. The research contributes to advancing technology-driven solutions for proactive security measures in high-risk environments, fostering safer and more secure institutional settings