Abstract - In this research, we present an innovative Accident Detection and Alert System that combines Deep Learning (DL) and Edge Computing technologies. The system is designed to process live video feeds from surveillance cameras using DL …

The first neural network (NN) is a recurrent network that analyzes the time-dependent sequence of the images within each video. The second takes the encoding of the first NN and builds a second NN that reflects which videos contain accidents and which do not. The resulting model enables a prediction of whether new dashcam footage has an accident.

To implement an automated-system for detection and reporting of unexpected accidents using deep learning. 1. To detect an accident on highways. 2. Provide an alert message to the most proximate control room immediately. 3. Design a low resource consuming accident detection system that can compute on cheap hardware.

Sep 29, 2022 · Automatic accident detection can shorten the response time of rescue agencies and vehicles around accidents to improve rescue efficiency and traffic safety level. The ability to detect and track vehicle can be used in applications like monitoring road accidents.

Whenever an accident occurs, it will detect the accident and immediately report about it to the nearby control room. The working of the system is based on deep learning techniques that use convolutional neural networks.

Mar 15, 2025 · This advanced accident detection system significantly improves road safety by enabling timely identification of traffic incidents and rapid emergency response.

In this paper, we build an intelligent traffic monitoring system using state-of-the-art deep learning models that aims to detect traffic accidents and the condition of traffic at the site in real-time so that specific measures can be employed to deal with the situation, ranging from deploying police vehicles to handle the amount of congestion to...

management systems. Parsa et al. (2019) apply deep learning techniques to real-time traffic accident detection using spatiotemporal sequential data. By leveraging advanced machine learning algorithms, the study aims to develop more accurate and efficient methods for detecting and predicting traffic accidents,

AcciVue integrates deep learning neural networks, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks[1], to analyze CCTV video feeds for timely accident detection.

We propose a vehicle crash detection system with sophisticated components to improvise vehicle crash detection performance. While most existing vehicle crash detection systems depend on single modal data, the proposed vehicle crash detection system uses an ensemble deep learning model based on multi-modal data.