Hi there! Click one of our representatives below and we will get back to you as soon as possible.

Drowsy Driver Detection using Representation Learning

Brand:Big Spur
Product Code:PROJ5668
Availability:In Stock
star_border star_border star_border star_border star_border
mode_comment0 reviews editWrite a review
  • 3,500.00INR

Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, driver drowsiness detection has been considered a major potential area so as to prevent a huge number of sleep induced road accidents. Driver fatigue is a significant factor in a large number of vehicle accidents. Fatalities have occurred as a result of car accidents related to driver inattention, such as distraction, fatigue, and lack of sleep. Studies and experiments have substantiated the fact that driving performance deteriorates with increased drowsiness. VLSI based DIP systems are to modify the internal circuit structure level and to optimize the circuit complexity level and to improve the system quality. Most digital image processing algorithms are specified with floating-point data types but they are finally implemented in fixed-point architectures in order to satisfy the cost and power consumption constraints of embedded systems. Our proposed work is to design a efficient VLSI architecture based digital image processing feature extraction (LBP + DFT) and classification (CNN) system using RPR truncation multiplier architecture. This work is to identify the face image and update person details effectively. Existing system is to design a VLSI based CNN classifier architecture. This architecture is used to effectively identify the disease types. Proposed system is to design a (LBP + DFT) feature extraction architecture and conventional neural network classifier architecture. Then to design CNN classifier VLSI architecture and to optimize the normal processing element and exponential processing element design. Then to apply the CORDIC function based shift and rotation architecture and to analysis the threshold function level between support and test vector data base ECG signal. Finally to identify the ECG signal type effectively and to calculate the VLSI architecture delay time, speed and power consumption level. This work is to optimize the circuit complexity level. Our proposed work is face images feature extraction and classification application process. This application uses person details identification process for this project. The proposed system is to increase the system speed for real time DIP application.

                      


Write a review

Please login or register to review

Our Specialization

PremiumSupport Service
(Based on Service Hours)

Premium Development Service
(Based on Requirements)

Voice Conference Video On Demand Code Customization
24/7 Support Remote Connectivity Document Customization
Ticketing System Project on Demand Zoom/Google Meet Explanation
Live Chat Support Single Point of Contact(SPOC) Whatsapp Support

OUR HIGHLIGHTS

Discover our highlights here! Our highlights provide accurate data to evaluate our standard. We provide an overview of our services which exhibits the following qualities. When it comes to quality, we at ClickMyProject believe in helping our clients to gives you the best-in-class services.

23+

Years of Experience

20+

Specialized Domains

7.5L+

Projects Reached

99.9%

Customer Satisfied

Call Us
+91 96777-48277
Email
info@clickmyproject.com
Send Message
+91 96777-51577

Tags: 2015, VLSI, machine learning

Free Website Hit Counter
Free website hit counter