Presentation - Smart Vigilance System.pptx

Presentation - Smart Vigilance System

1. Mini Project Smart ‘Driver Vigilance System’ Project Presentation with Problem & Solution A proposed system aims to prevent truck accidents by detecting driver drowsiness through machine vision-based concepts such as eye identification, tiredness recognition, and eye squinting pattern recognition.

2. Highway Truck Accidents Problem Both driver drowsiness and distraction can have similar effects, such as reduced driving performance, delayed reaction times, and an increased risk of being involved in a crash. Driver Fatigue Resulting from Sleep Disorders 01 02 03 How big is the problem Why driver becomes Drowsy Mini Project It an important factor in the increasing number of accidents on today's roads. Studies suggest that up to 20% of road accidents are due to exhaustion, with the number rising to 50% on some highways. When drivers continuously drive without breaks, they are at high risk of becoming drowsy.

3. Vigilance System Solution Driver drowsiness detection is a safety innovation that prevents accidents caused by driver fatigue. Studies suggest that up to 20% of road accidents are due to exhaustion, with the number rising to 50% on some highways. Mini Project Proposed Method 01 02 How it detects drowsiness The project aims to analyze past research, propose a method using a camera for detecting drowsiness. Detection of driver drowsiness through eyelid and face region analysis.

4. Software Requirements The requirements of software to run this system. Python is an interpreted high-level programming language for general-purpose programming. OpenCV (Open-Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision. The supported Operating Systems for client include: Windows 2010, windows 2008, windows 2007. Python OpenCV & Keras Requirements to Run Mini Project

5. Hardware and Software PROPOSED SYSTEM Methods Mini Project The requirement for this Python project is a webcam through which we will capture images. This Project needs a Alarm which alerts the driver if it detects danger. OpenCV enables computer vision analysis, TensorFlow supports machine learning model development, and Keras simplifies neural network implementation for accurate detection algorithms.

6. Flow Diagram Mini Project

7. System Flow Mini Project

8. Code Snippets Mini Project

9. Result Mini Project

10. Future Scope The future scope of the "Smart Driver Vigilance System" is promising, with several avenues for further development and enhancement . “We can Develop analytics to understand long-term driver behavior patterns, aiding in the creation of personalized monitoring solutions” Biometric Expansion 01 02 03 Advanced Machine Learning Cross-Platform Compatibility Mini Project Extend monitoring to include additional indicators such as heart rate and skin conductance for a more comprehensive assessment of driver alertness Continuously refine machine learning models to improve accuracy and adapt to new driving conditions and environments Ensure the system is compatible with a variety of vehicle models and platforms for broader adoption and applicability

11. Bibliography Mini Project Kumar, R., Kumar, V., & Choudhary, A. (2023). Driver Drowsiness Detection Using Deep Learning. https://arxiv.org/ftp/arxiv/papers/2303/2303.06310.pdf Sinha, D., & Sarkar, S. (2021). Driver Drowsiness Detection Using Image Processing and Machine Learning. International Journal of Engineering and Advanced Technology. https://www.ijeat.org/wp-content/uploads/papers/v11i1/A31591011121.pdf Driver Drowsiness Detection Using Machine Learning. (n.d.). International Journal of Research in Applied Science and Engineering Technology https://www.ijraset.com/research-paper/driver-drowsiness-detection-using-machine-learning Verma, M., & Thakur, N. (2021). Driver Drowsiness Detection using Eye Aspect Ratio and Sound Alarm. International Research Journal of Engineering and Technology (IRJET), 8(8). https://www.irjet.net/archives/V8/i8/IRJET-V8I8350.pdf 5. Vignesh, N., et al. (n.d.). Driver Drowsiness Detection. Sathyabama Institute of Science and Technology (SIST). https://sist.sathyabama.ac.in/sist_naac/documents/1.3.4/1822-b.e-cse-batchno-129.pdf

12. Our team ADARSH MOURYA EN21CS301008 EN21CS301028 EN21CS301002 AARYAN SAHU AADI SHARMA

13. Thank You Mini Project