Survey of Various Methods used for Speed Calculation of a Vehicle

It is a survey paper of various method used for speed calculation of vehicles. The major purpose of vehicle speed detection is to provide a number of ways that law enforcement agencies can enforce traffic speed laws. The most famous methods include using RADAR (Radio Detection and Ranging) and LIDAR (Light Detection and Ranging) devices to detect the speed of a vehicle. RADAR use microwaves pules and LIDAR use coherent light beam for speed calculation. The SDCS (Speed Detection Camera System) and SMBI (Single Motion Blurred Image) method are also use on high traffic area to measure speed of vehicle using video stream and single image captured by stationary camera. DOI: 10.17762/ijritcc2321-8169.150314

Influence of truck driver eye position on effectiveness of retroreflective traffic signs

The amount of light reflected from a retroreflective traffic sign decreases with an increase in the observation angle—the angle between the headlamp, the sign, and the eyes of the driver. Mainly because of the increased seated eye height of truck drivers, the actual observation angles are greater for them than they are for car drivers. Consequently, there is concern about the impaired night-time detection and legibility of retroreflective signs for truck drivers. The present study evaluated the relative amount of light reaching drivers of different types of vehicle by using survey data collected in 1989 by the Transport and Road Research Laboratory (TRRL) in England. The TRRL data included driver eye heights and headlamp mounting heights for 445 vehicles. The present analysis considered three sign locations on a straight roadway: left shoulder, centre, and right shoulder. Two viewing distances were included: 152 m (500 feet) (typical of a sign-legibility distance), and 305 m (1000 feet) (typical of a sign-detection distance). The analysis considered both the differential amount of illumination impinging on the signs from headlamps of trucks and cars, as well as the differential amount of the light reflected from the signs in the direction of truck drivers and car drivers. The main results are that for the viewing distance of 152 m, the amount of light reaching a truck driver can be as low as 25% of the light reaching a car driver; the corresponding percentages for the viewing distance of 305 m are as low as 68%. These reductions were then related to the expected effects on sign legibility and detection. The results imply that the increased eye height of truck drivers could have a major effect on the legibility of retroreflective traffic signs, but only a modest effect on their detection.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68794/2/10.1177_096032719302500105.pd

Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time -- the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for model adaptation from daytime scenes to nighttime scenes, without using extra human annotation.Comment: Accepted to International Conference on Intelligent Transportation Systems (ITSC 2018