Machine vision of traffic state estimation using fuzzy logic

• Main Authors: Quiros, Ana Riza F., Bedruz, Rhen Anjerome, Uy, Aaron Christian P., Abad, Alexander C., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2017
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1041; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2040/type/native/viewcontent
• Institution: De La Salle University

© 2016 IEEE. One of the problems encountered by motorists are congested roads. Current technology cannot easily broadcast the information about which roads are heavily congested and which are not to the motorists. As such, planning of the route to take to their destinations is compromised. This paper proposes a fuzzy logic method approach to the estimation of the traffic state of a road. Images from IP cameras installed in different roads can be used to determine the state of the traffic in an area at any point in time. The vehicles within the image are needed to be detected first via edge detection. As the vehicles are detected within the image, so are their position and size with respect to the whole image are obtained. As such, three different parameters namely vehicle density, distance between neighboring vehicles and vehicle sizes can be computed. Using these three parameters, a fuzzy logic system can be created. Three degrees of intensity for each parameter was used, creating 27 rules. The center of gravity method was used to defuzzify the traffic density parameter. Based on the results, the designed algorithm was able to identify six different road images of different traffic states accurately.