You must complete each of the steps listed below in the exact same order as stated in order to run this project on your local system.
- Fork this repository using
- Run this command to clone the repository
https://github.com/shivam6167/AI-Monitoring-and-Response-system-for-Anti-Accident-Mechanism.git - Create a new virtual environment using the command
virtualenv env - Activate the environment using the command
env\Scripts\activate.bat - Install and import all the libraries
The AI Monitoring and Response System for Anti-Accident Mechanism is a revolutionary solution designed to promote safe driving practices and to prevent and encounter accidents in challenging driving conditions, etc, especially in hilly areas. The model monitors driver's activity throughout the driving and analyses the gesture patterns to foster a safe and healthy driving experience and reduce risks. The system is specifically tailored to address the unique risks and hazards associated with driving in hilly regions.The system is built on a foundation of state-of-the-art deep learning algorithms that analyze drivers' facial expressions, eye movements, head postures, and driving patterns. The system can detect signs of driver distraction, fatigue, and other unsafe behaviors that can lead to accidents.
When the system detects any such signs, it sends an alert to the driver, prompting them to take corrective action or stop driving altogether. In addition, the system can also send an alert to a pre-defined emergency contact, such as a family member or the local authorities, in case of a severe emergency or accident.The AI Monitoring and Response System for Anti-Accident Mechanism in Hilly Regions is an accessible and cost-effective solution for all types of vehicles, including cars, buses, and trucks. It is particularly useful in mountainous areas, where accidents are more common due to challenging driving conditions.Overall, the AI Monitoring and Response System for Anti-Accident Mechanism, specially in Hilly Regions is a game-changer in promoting safe driving practices and preventing accidents in challenging driving conditions. It has the potential to save countless lives and prevent thousands of injuries on the road.
Our methodology for the AI-Monitoring-and-Response-system-for-Anti-Accident-Mechanism in Hilly Regions involves the use of video processing techniques, neural networks, and Deep Learning to record and analyse the driver's behaviour. We are using a combination of a dashboard-mounted camera and face recognition to monitor the driver's head movements, facial expressions, and eye movements. To ensure the effectiveness of our system in real-world conditions, we are also collecting our own dataset using our own head movements and facial expressions. We will be using a collection of driving scenarios to train our deep learning algorithm on various patterns of behaviour that may lead to an accident. Twilio will be used to generate alerts for the driver's relatives in case the system detects risky behaviour. The system will then analyse the data to determine whether the driver is engaging in any unsafe conduct and notify them to stop. Through continuous monitoring and alerting, our system aims to prevent accidents and ensure safe driving in most of the scenario.
With the Ai Monitoring and Response system for Anti-Accident Mechanism, which is a super-efficient integration of Deep Learning with Artificial Intelligence. We aim to uplift the standards of living by procuring an efficient mechanism of saving lives with the help of anti-accident mechanism using Deep Learning Algorithms. According to NHAI, every 1 of 4 deaths are due to road accidents, this condition becomes serious in hilly areas which has contributed to 40 percent of it! We aim to craft a solution that monitors and respond based on drivers’ behaviour. We analyse the moments and gestures of the person and our model automatically predict happening of event, thus preventing it by alerting more efficiently.
Beyond analysing the behaviour and creating an monitoring and responsive environment to prevent accidents, we also readdress in times of happening and dealing with consequences, though we try to prevent by alerting the driver with “beep” and notifying and suggesting with various suggestion from Ai like “Turning on the lights, Shifting the music”, etc. However, in instances of accidents, this mechanism sends prompt to Authorities and Family members with location so prompt and appropriate cure can be taken and millions of lives can be saved! Our USP is not only being cheap and effective but also the model presented is unique to any existing mechanism till date.
Our model has the potential to make a significant positive impact on traffic safety by helping to prevent accidents caused by human error such as distracted or drowsy driving. By constantly monitoring the road and surroundings and making more accurate decisions, the technology could potentially reduce the number of fatalities and injuries on the roadways, improving the overall quality of life for citizens. Automobile makers and transportation providers could also benefit from implementing the technology by improving their products and services, increasing customer satisfaction and safety ratings, and gaining a competitive edge in the market. Our model has the potential to contribute to a safer, more efficient, and stress-free driving experience for everyone.
Here is the link that you can use for accesing the code file 👉 https://github.com/shivam6167/AI-Monitoring-and-Response-system-for-Anti-Accident-Mechanism.git
Here is the link 👉 https://youtu.be/v_JFj6vRCnc
Here is the link 👉 https://drive.google.com/drive/u/0/folders/10QtkedmhOD-FQT0QVMWB6hxhy6sZ6fuN