COMPUTER VISION BASED TRAFFIC RULE BREACH DETECTION

This paper is about the detection of traffic rule breach via computer vision which takes the feed from the traffic surveillance system, processes the video feed, detects the breach and alerts the traffic police. The number of traffic accidents is on the rise with the increasing number of vehicles. Traffic breach is the biggest cause of accidents. So, to mitigate this problem our system processes the CCTV camera feed in real-time, detects the traffic rule breach events and sends the push notification to the android based application of the traffic police stationed nearby; so, further actions can be taken. As this system detects breach faster than humans, the concerned authoritarian department will be at ease in implementing safe roads accurately. This system acts as an add-on to the current video surveillance system rather than building new infrastructure. Thus, the output of this system can be used not only or safety and security purposes but as well as for analytical purposes with effective traffic monitoring at a lower cost. Hence, this system aids law enforcement agencies in implementing road safety efficiently and effectively ensuring smooth traffic flow.

Smart Transportation System using IOT

In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.

A REVIEW STUDY OF TRAFFIC SIGNAL VIOLATION DETECTION USING ARTIFICIAL INTELLIGENCE

In the new evolving world, traffic rule violations have become a central issue for majority of the developing countries. The numbers of vehicles are increasing rapidly as well as the numbers of traffic rule violations are increasing exponentially. Managing traffic rule violations has always been a tedious and compromising task. Even though the process of traffic management has become automated, it’s a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The principal objective of this project is to control the traffic rule violations accurately and cost effectively. The proposed model includes an automated system which uses IR sensors and camera based on Raspberry PI to capture video. The project presents Automatic Number Plate Recognition (ANPR) techniques and other image manipulation techniques for plate localization and character recognition which makes it faster and easier to identify the number plates. After recognizing the vehicle number from number plate, the SMS based module is used to notify the vehicle owners about their traffic rule violation. An additional SMS is sent to Regional Transport Office (RTO) for tracking the report status. KEYWORDS- Automatic Number Plate Recognition (ANPR), Artificial Neural Network, Image acquisition, CNN, Tesseract OCR, Canny Edge Detection.

Risk Perceptions of Crash Related Traffic Rule Violations

The objective of this paper is to evaluate drivers’ risk perception toward crash related traffic rule violations and identify violations that are perceived as low risk to better educate drivers. Risk perceptions on crash related traffic rule violations was gathered from 3,593 participants as a part of Naturalistic Driving Study. The variations in risk perceptions by driver characteristics such as age, gender, education, and household income were studied. The risk perception of violating traffic rules was observed to increase with an increase in drivers’ age, except for driving under the influence of alcohol and drugs. Drivers older than 25 years perceive disregarding traffic signals as the riskiest traffic rule violation. Exceeding speed limit by 10 to 20 mph is perceived as the least risky among the considered traffic rule violations, irrespective of age, gender, education, and income level of the driver. The risk perception of disregarding traffic signals and following vehicle closely are statistically the same for both male drivers and female drivers. For all other traffic rule violations, female drivers’ risk perception is greater than male drivers’ risk perception. Participants with lower education level perceive violating traffic rules as not risky, except for DUI. Graduates or professionals with no advanced degree perceive risk of violating traffic rules greater than the average risk for the entire sample population. Dissemination of risk perception information as well as enhanced educational programs are necessary to increase awareness about the risk associated with violating traffic rules that are perceived as low risk by drivers.