In today’s transportation landscape, road accidents remain a significant concern, necessitating swift and efficient responses. Traditional reporting methods reliant on human observation often lead to delays, impacting accident victims. To address this, we propose an intelligent Accident Detection system, leveraging real-time video analysis to promptly identify and classify accidents. By eliminating manual reporting, the system aims to minimize response times and enhance road safety. Using advanced computer vision, it swiftly recognizes various accident types and triggers immediate alerts to authorities and emergency services. This integration facilitates rapid and well-coordinated responses, potentially reducing accident impacts. Overall, the system promises transformative impacts, improving emergency management and ultimately saving lives on our roads.
1. Accident Detection:
Develop a CNN using OpenCV and TensorFlow to detect and classify accidents with a high accuracy range (95-100%) involving cars, bikes, and pedestrians in real-time.
2. Distance Monitoring and Risk Assessment:
Utilize a Cascade Classifier and Car.xml file to measure distances between vehicles, providing alerts and aiding preventive measures when vehicles approach closely.
3. Accident Severity Prediction:
Predict accident severity based on the speed of vehicles colliding, calculated in pixels per frame, for effective prioritization of emergency responses.
4. Alert and Emergency Response:
Trigger immediate email notifications to the nearest hospital upon accident detection, employing a secure SMTP protocol. Confirmations ensure timely initiation of emergency procedures.
5. Firebase Integration for Real-time Data:
Integrate with Firebase to provide real-time accident prediction values and live images, facilitating efficient data storage and retrieval for emergency responders and authorities.
Objectives:
1. Accurate Accident Identification:
Develop a CNN for precise real-time detection of accidents involving cars, bikes, and pedestrians with a high accuracy range (95-100%).
2. Proactive Distance Monitoring:
Implement a system to monitor distances between vehicles, issuing alerts to prevent potential accidents when vehicles approach closely.
3. Severity Prediction for Quick Response:
Predict accident severity based on collision speed in pixels per frame, enabling swift and prioritized emergency responses.
4. Immediate Hospital Alerts:
Trigger instant email notifications to the nearest hospital upon accident detection, ensuring prompt initiation of emergency procedures.
5. Real-time Data Integration with Firebase:
Integrate with Firebase to provide live accident prediction values and images, facilitating efficient data storage and retrieval for emergency responders.
We have collected a dataset comprising accident and non-accident images, which we utilize for our task. We train, test, and validate the images, organizing them into subfolders with their respective labels. Following this, we train our model using the image data and fit it accordingly. Our system consists of three parts:
For this purpose, we utilized OpenCV and TensorFlow to train a Convolutional Neural Network (CNN), a deep learning model, capable of detecting accidents between cars and cars, cars and bikes, and cars and pedestrians. Our model is trained and fitted using our dataset of images. Consequently, when the system analyzes real-time video feed capturing two cars approaching each other, or a car approaching a pedestrian or a bike, it can detect the possibility of an accident. As the vehicles or individuals come closer to each other, the accident percentage increases. This approach enables the system to provide predictions with a high level of accuracy, ranging from 95 to 100 percent, regarding the occurrence of an accident.
Typically, when two vehicles approach each other closely, the risk of an accident occurring increases. To address this, we utilize a Cascade Classifier, which is a machine learning algorithm, and leverage a Car.xml file. This algorithm aids us in measuring the distance between two cars on the road by employing Euclidean distance calculations. With this approach, we can detect the presence of vehicles and calculate the distance between them. This allows us to receive alerts and take appropriate actions to prevent potential accidents.
In this part we will predict severity of accident based on speed of vehaicles bouncing with each other. Here speed is calculated in term of pixels per frame. And this can tells us Level of Severity of accident.
Once an accident is detected and classified, the system triggers an immediate email notification to the nearest hospital. To ensure the delivery of notifications, we employ a secure and reliable SMTP (Simple Mail Transfer Protocol). This protocol guarantees the successful transmission of the email notification. Upon receiving the email, the hospital is expected to acknowledge its receipt by sending a confirmation. This confirmation serves as assurance that the hospital has been alerted and can initiate their necessary emergency response procedures accordingly.
We will connect our system with firebase. So it will sent Real time database values of accident prediction on firebase database .And got alerted either it is accident between car and car or other one. Also we can get live images of accident at the spot when accident happened.
The GUI for Accident Detection and Distance Measurement, developed using Tkinter, aims to provide a user-friendly interface that seamlessly integrates the functionalities of accident detection and distance measurement. Leveraging Tkinter, we will create an intuitive and visually appealing graphical user interface that includes features such as live video display, real-time alerts, and configuration options. This GUI will enhance the usability and effectiveness of the system, enabling users to interact effortlessly with the accident detection and distance measurement capabilities.
The development of an Accident Detection System using computer vision and deep learning techniques can significantly enhance road safety by enabling real-time detection of accidents. We have successfully built an accurate and reliable accident detection and Alert system. The integration of real-time alert mechanisms and continuous monitoring ensures prompt response and potentially saves lives. Deploying and maintaining this system can contribute to improved road safety and emergency management.