Driver Assistance
Resources: Website
This feature uses camera in order to capture the eye movement of driver so that I will ensure whether driver is driving with full concentration or fall asleep
On the top-left we have an eye that is fully open with the eye facial landmarks plotted. Then on the top-right we have an eye that is closed. The bottom then plots the eye aspect ratio over time.
As we can see, the eye aspect ratio is constant (indicating the eye is open), then rapidly drops to zero, then increases again, indicating a blink has taken place.
Using geo fencing- Automated toll payment Website - Code have to check
Developing countries like India needs a significant improvement in infrastructure such as Roads or Highways. Construction of these highways is a costly affair, which can’t be invested by the government alone. Normally Public private partnerships are made to construct such a huge projects. The money spent on these projects can be regained by collecting toll from the passengers who use the roads. The toll collection system, especially in India faces some problems such as long queue lines, escaping from toll plazas etc. These systems can service only 300 vehicles per hour, and if more than that number of vehicles arrive at that plaza, server traffic jams may occur. To solve this we are proposing to create geo-fences using GPS by giving latitude and longitude of the corner of the toll plaza. By comparing the position of the vehicle and toll plaza, the owner of the vehicle can be charged from the account
This case study evaluates the ability of the TensorFlow* Object Detection API to solve a real-time problem such as traffic light detection. The experiment uses the Microsoft Common Objects in Context (COCO) pre-trained model called Single Shot Multibox Detector MobileNet from the TensorFlow Zoo for transfer learning. Intel® Xeon® processor-based machines were used for the study. At the end of this experiment, we obtained an accurate model that was able to identify the traffic signals at more than 90 percent accuracy.
The aim of the project is to detect and recognize traffic signs in video sequences recorded by an onboard vehicle camera. Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. This paper presents a study to recognize traffic sign patterns using openCV technique. The images are extracted, detected and recognized by pre-processing with several image processing techniques, such as, threshold techniques, Gaussian filter, canny edge detection, Contour and Fit Ellipse. Then, the stages are performed to detect and recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show the highly accurate classifications of traffic sign patterns with complex background images and the computational cost of the proposed method
Traffic sign recognition.
In the 2020 Honda Accord models, a front camera sensor is mounted to the interior of the windshield behind the rearview mirror.
That camera polls frames, looks for signs along the road, and then classifies them.
The recognized traffic sign is then shown on the LCD dashboard as a reminder to the driver.
It’s admittedly a pretty neat feature and the rest of the drive quickly turned from a vehicle test drive into a lecture on how computer vision and deep learning algorithms are used to recognize traffic signs