Detection and Alert generation of road accident using deep learning

This research endeavors to address the critical issue of Road Accident Detection, presenting novel solutions to the identified challenges. The paper introduces an advanced framework specifically tailored for the efficient detection of accidents at intersections within traffic surveillance applications. Emphasizing the pivotal role of automatic accident detection in contemporary traffic monitoring systems, the study explores the integration of computer vision techniques, particularly within the context of city intersections connected to traffic control systems through security cameras. The automatic detection of traffic accidents is a prominent area of research in traffic monitoring systems. These days, traffic control systems are connected to many city intersections via security cameras. Recognizing traffic accidents as a leading cause of daily fatalities, attributed primarily to driver errors and delayed emergency service responses, this research endeavors to contribute to the field by proposing a system that enhances safety measures. The proposed system employs cutting-edge video analysis techniques to automatically identify accidents or potential incidents in real-time. Furthermore, the incorporation of advanced technologies such as Long Short-Term Memory, Convolutional Neural Network, and Recurrent Neural Network aims to elevate the efficiency of the detection process. The research not only advances the state-of-the-art in accident detection but also holds promise for practical implementation in real-world traffic management scenarios. These days, traffic accidents rank among the leading causes of fatalities on a daily basis. The driver's mistake and the emergency services' slow response time are the main causes of it. This system aims to enhance safety measures by automatically identifying accidents or potential incidents in real-time using video analysis techniques and quickly informing pertinent stakeholders.