Machine Vision to Alert Roadside Personnel of Night Traffic Threats (original) (raw)

Vehicle Driver Warning Systems Using Road Marking and Traffic Light Detection

https://www.ajer.org/current-issue.html, 2022

Everyone has experienced fatigue and sleepiness while driving. This makes him not know the direction so that it violates traffic and can cause an accident. Violations that usually occur are breaking through traffic lights and violating road markings. Therefore, a simulation software was made to help negligent and sleepy drivers not to violate traffic and reduce accidents. The technology used is image processing with C# programming and the EmguCV library using the Haar Cascade Classifier and Color Detection methods. Haarlike features are rectangular features, which give a specific indication of an image. The captured image will be processed in two stages, namely preprocessing to detect markings and Gaussian filter to detect traffic lights. The results of the preprocessing will be processed in the Haar Cascade Classifier to get the ROI of the marker and then look for the coordinates to find the distance between the marker and the driver. The limit used in measuring distance is 25.57 cm (85 pixels). If the coordinate distance is less than 25.57 cm, the alarm will sound and alert the driver to stay away from the marker and if the coordinate distance is more than 25.57 cm, the alarm will be off. While the results of the gaussian filter will be converted into HSV frames to detect red and green colors using the color pixel values of each color. The color of the light can be detected when the contour size value is between 0 and 6.

Real-time vision system for nighttime vehicle detection

2017

In recent years, the number of cars in the world has been increasing, resulting in a rise in the importance of video-based traffic flow monitoring and counting technology. However, compared with the computerized vision-based traffic flow monitoring and counting technologies used during daytime, those used during nighttime are less developed. In view of this, we have proposed a multi-feature technology, which can be used to monitor the headlights of cars during nighttime. First, the light source distribution in each captured image is analyzed and the length-width ratios of the headlights are distinguished. If two headlights both match the standards, a comparison between cars is carried out in order to avoid counting the same car more than once. The space between and colors of the two headlights are then used to judge whether these two lights belong to the same car using a similarity analysis algorithm. Experimental evaluations have shown that the proposed technology in this study can...

IJERT-Surveillance system for automobiles

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/surveillance-system-for-automobiles https://www.ijert.org/research/surveillance-system-for-automobiles-IJERTV2IS111003.pdf Student ,M.Tech[embedded system design], kuppam engineering college ,kuppam, Andhra Pradesh. Abstract Now a days, Innovation is in the side of utilizing the common needs and not on our safety measurements that which we need to consider. As a common example consider, Road accidents, which was getting to be happen in our day to day life. Due to driver's loss of attention, most of the accidents takes place. As a measure to overcome from this, the visual information of driver will be monitored for the driver attentiveness in cars using this project that which implemented in real time. Our project main intension is to design and develop a low cost featured device which is based on embedded platform for finding the driver drowsiness. Specifically, our Embedded System includes a webcam placed on the steering column which is capable to capture the eye movements of the Driver to find out fatigue. If the driver is not paying attention on the road ahead and a dangerous situation is detected, the system will warn the driver by giving the warning sounds.

Computer vision and its role in driving safety

International Journal of Advance Research, Ideas and Innovations in Technology, 2020

Every year there are more than 1.2 million road accidents happening across the globe, which accounts for more than 2.2% of deaths on a global scale. There has been an alarming increase in road accidents in today’s time and a major reason behind this can be attributed to how the driver is behaving during his driving. Some of them may be unavoidable, yet a major portion of the hazards may be averted if there are means to keep a check on driver state ranging from their physical condition to monitoring their reckless driving patterns. This is where the advent of technology and the role of having a robust monitoring ecosystem come into the picture. Computer Vision more or less is a sought after technology that automotive companies today are chasing, be it telematics-based connected cars or autonomous self-driving vehicles. It can help solve this purpose by monitoring the driver drowsiness through advanced image processing solutions and providing the user with an integrated report showcas...