Machine vision for traffic violation detection system through genetic algorithm

• Main Authors: Uy, Aaron Christian P., Bedruz, Rhen Anjerome, Quiros, Ana Riza, Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2016
• Subjects: Computer vision; Traffic violations; Intelligent transportation systems; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2093; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3092/type/native/viewcontent
• Institution: De La Salle University

This paper presents a machine vision algorithm to detect traffic violations specifically swerving and blocking the pedestrian lane. The proposed solution consists of background difference method, and focuses on the genetic algorithm of the system to detect these violations. The general process is as follows: a capture picture is to be subtracted first by the reference image, then the genetic algorithm is run to find the violator, and finally a display is outputted with the corresponding type of violation. The machine vision traffic violation detection system was found to have an average convergence of about 8 iterations, within an average of less than 300 generations. These results show that the algorithm is well-suited for real time implementation in traffic detection system. Provided the system inputs were captured photos from a CCTV camera, whereas the outputs were cropped pictures of the car that was detected to have such violations mentioned earlier. © 2015 IEEE.