Accident Detection Using Convolutional Neural Networks

— to develop a CNN model for accident detection, a
large dataset of accident and non-accident images will be
required for training and testing the model. The dataset should
be diverse and cover a range of accident scenarios to ensure that the model is robust and can detect different types of accidents. Once the CNN model is trained, it can be used to process the live video feed from the CCTV camera installed on the highway. Each frame of the video can be passed through the CNN model to classify it as an accident or non-accident frame. If an accident is detected, an alert can be sent to the nearest emergency services or to a central control room to initiate the required rescue operation. One potential challenge with this approach is the need for real-time processing of the video feed to ensure that accidents are detected promptly. This may require high-performance computing hardware and optimized software algorithms to ensure that the CNN model can process the frames of the video in real-time. The proposed system to detect accidents based on the live feed of video from a CCTV camera using a deep learning convolution neural network model is a promising approach. CNNs have indeed been shown to be highly effective for image classification tasks and have been successfully used in many applications, including object detection and recognition. In summary, the proposed system using a CNN-based model to detect accidents based on live video feed from CCTV cameras has great potential to reduce the number of accident-related deaths in India by enabling timely help to reach accident victims. However, the development and implementation of such a system will require significant resources and expertise in computer vision, deep learning, and real-time processing