Automated traffic violation apprehension system using genetic algorithm and artificial neural network

• Main Authors: Uy, Aaron Christian P., Quiros, Ana Riza F., Bedruz, Rhen Anjerome, Abad, Alexander C., Bandala, Argel A., Sybingco, Edwin, Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Pattern recognition systems; Intelligent transportation systems; Traffic violations; Neural networks (Computer science); Genetic algorithms; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1935
• Institution: De La Salle University

Developing countries face the problem of crowded and congested roads because of inefficient implementation of traffic rules. Motorists ignore the rules because they are not apprehended and can get away easily. This paper proposes an intelligent traffic system that is able to automatically detect and apprehend traffic violators, specifically motorists who either swerve or block the pedestrian lane. The system is designed by integrating three processes: violation detection, plate localization and plate recognition. The violation detection and plate localization were realized using genetic algorithm while the plate recognition process was performed using an artificial neural network. The recognition of the plate number is highly dependent on the position of the detected vehicle with respect to the camera. Thus, the recognized plate number will only be supplementary information about the violator; the physical attributes of the vehicle which is captured by the violation detection process will be the main information on the violator. Based on the results of 48 images tested, the overall system was able to detect the mentioned violations and to identify the plate number of the vehicles that were detected as traffic violators, with an average accuracy of 90.67%, and program runtime of 1.34 seconds. © 2016 IEEE.