Fatigue detection in real-time for driver monitoring systems
A real-time fatigue detection can improve public safety by identifying early signs of drowsiness during driving. By analyzing various patterns (eye closure, blinking frequency, head movement, and yawning) through camera, it detects alarming conditions and triggers instant alerts to prevent potential accidents.
The technology can benefit safety-critical industries by combining continuous monitoring, data-driven insights, and compliance.
Fatigue, among other factors, is one of the major issues that cause road accidents, in particular fatal outcomes.
To change this tendency, our talented R&D team has brought an algorithm to assess driver drowsiness to life. Read further to discover how our R&D engineers have used computer vision and related advanced techniques to deliver a solution that alerts drivers about potential risks.
What is a fatigue detection system?
Our fatigue detection software is an AI-based solution specifically designed to monitor the signs of drowsiness. The advanced monitoring system is using computer vision, landmark detection, facial mapping, haar cascades, and other complex techniques to prevent road accidents.
The fatigue detection software is built to analyze frequent blinking, eye closure, and similar warning patterns. That means promising opportunities for fleet management solutions and workspace protection programs.
How does a fatigue detection system work on the road?
The fatigue detection software is installed in vehicles and utilized in-cabin cameras and custom AI algorithms. By identifying facial landmarks, it analyzes several patterns to determine the driver’s current state and triggers an alert to prevent any hazard.
The fatigue driving detection can be also integrated with sensors to analyze other patterns when requested. These include lane deviations, erratic steering, and other driving changes.
Why safety is critical to the automotive industry
As modern self-driving vehicles are transforming the entire automotive industry, driver safety becomes critical. It stands for readiness to integrate innovative technologies, business opportunities, future-proof investments, and compliance with established regulatory requirements.
The modern-world automotive industry (as well as construction, energy & utilities, manufacturing, and others) is a safety-critical segment in which the consequences of failure or malfunction might include lasting damage, serious injury, and even fatal outcomes.
The domain is at higher risk for dangerous, fatigue-related incidents and injuries than any other sector.
A driver monitoring system can change the game by minimizing potential injuries in the safety-critical industry. That’s why we harnessed computer vision to explore its possibilities.
Road safety by using advanced fatigue detection technologies
- Early detection – the system identifies drowsiness through real-time facial analysis
- Instant alerts – the system sends alerts to drivers to prevent potential accidents and trauma
- Regulatory compliance & security – the tool can also help companies to optimize rest schedules, thereby improving working conditions
- Data-driven insights – if necessary, the system can provide detailed reports to enhance staff training
Efficiently managing driver tiredness with fatigue detection software
Abto Software’s driver fatigue detection system is a computer vision, camera-based technology that assesses driver alertness by monitoring the set of symptoms we highlighted in the table below:
| Fatigue symptom | Measurable indicator |
| Falling asleep | The duration of driver’s eyes shut |
| Excessive blinking | The frequency of blinking |
| Eyelid closure | The ratio of driver’s eyes closed |
| Frequent yawning | The ratio of yawning |
| Head tilting | The ratio of tilting |
The video below explains and illustrates the concept of the delivered solution (turn on the subtitles to access real-time explanations of how each symptom is identified and tracked):
The legend to understand the visualization:
| 1. Number of fatigue symptoms and how they’re detected 2. Detected face (green box) 3. Eye aspect ratio (EAR) 4. Eye regions (green contour) 5. Head orientation vector (HOV) 6. Detected eyes (purple boxes) 7. Mouth region (yellow contour) 8. Face detection algorithm accuracy 9. Head tilt angle (absolute angle) 10. Closed eyes duration | 11. Closed eyes ratio 12. Blinking frequency 13. Percentage of eye closure 14. Head tilt ratio 15. Head tilt tangent 16. Yawn ratio 17. Mouth aspect ratio (MAR) 18. Eye aspect ratio graph 19. The inverted EAR graph for EAR < 0.3MAR graph 20. Head tilt angle graph |
Our fatigue detection technology tech stack
Abto Software’s driver fatigue monitoring system tech stack in details:
OpenCV
OpenCV is the backbone of our driver fatigue detection system, which provides the tools to enable:
- Image processing
- Video analysis
- Face detection
- Feature extraction
OpenCV allows the solution to track facial expressions (in particular, eye movement) to detect critical patterns.
Python
Python empowers the entire driver fatigue monitoring system:
- By integrating AI models
- And processing data extracted from streams
Python’s libraries and frameworks are perfect for rapid software development and deployment of applications that leverage machine learning.
dlib
dlib helps with precise facial mapping by detecting key landmarks (eyes, eyebrows, nose, mouth, and others). It’s used to track eye blinking, yawning patterns, and other key indicators of fatigue.
DNNs
DNNs (Deep Neural Networks) are used to process facial features to recognize subtle patterns of drowsiness. These analyze any signs (eye blinking, head tilting) to detect a signal of fatigue with accuracy.
Facial mapping
Facial mapping involves identifying and tracking facial points to assess driver alertness by analyzing:
- Eye blinking (lid movement)
- Yawning patterns (mouth movement), and other important features
Haar cascades
Haar cascades are object detection algorithms we used to locate the faces of drivers on the video streams. They help efficiently detect eye and mouth movement, mainly acting as an initial filter before analysis.
How we can help
By integrating AI-based technology into fleet management systems, you ensure better safety, less accidents, and compliance with industry-specific regulatory requirements.
Contact us to reshape your operations and unlock business growth!
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- AI development
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FAQ
Our tool is built by using:
- OpenCV
- Python
- dlib
- DNNs
- Facial mapping
- Haar cascades
Modern tools are designed to function in various lighting conditions, which include low-light/nighttime driving. Some leverage infrared cameras to track facial features, which ensures the solution remains efficient no matter the external lighting conditions.
The accuracy of driver fatigue detection largely depends on the used technology, environmental conditions, and how well the AI model is trained.
As soon as driver fatigue detection is triggered, it alerts the driver to take corrective action or to stop driving. This can be done through audio, dashboard notifications, seat vibrations, and steering wheel resistance.
Discover how driver fatigue monitoring software can enhance road safety by identifying any signs of drowsiness in real-time by using computer vision.
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