Intelligent Image Classification and Emergency Detection for Enhanced CCTV Surveillance Systems using CNN

Highways pose significant dangers during night time due to low visibility and increased dangers, demanding urgent emergency measures. To address these safety concerns, we propose a model solution aimed at enhancing nighttime highway safety through the utilization of existing CCTV camera infrastructure. Our project aims to develop a model that can be easily integrated with existing CCTV cameras to assist individuals in distress, with a specific focus on vulnerable girls and boys facing potential danger. The model employs cutting-edge technologies such as, computer vision and artificial intelligence techniques to recognize distress signal for help gesture, separate from standard sign language. By continuously analyzing real-time videos from the CCTV cameras, the model swiftly detects distress signal. Upon detection, the system can be utilized to send relevant information, such as captured videos, to the closest police station or helpline officers, ensuring immediate action. Our main objective is to provide an easy and quick solution for travelers, by bridging critical response gaps during emergencies, providing essential assistance to those who are in helpless situations. Through the power of technology, our mission is to create a safer highway environment and improvement in the wellbeing of individuals traveling on roads at night.