Machine vision for traffic violation detection system through genetic algorithm

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

8th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2015

Publication Date

1-25-2016

Abstract

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.

html

Digitial Object Identifier (DOI)

10.1109/HNICEM.2015.7393241

Disciplines

Electrical and Computer Engineering

Keywords

Computer vision; Traffic violations; Intelligent transportation systems; Intelligent transportation systems

Upload File

wf_yes

This document is currently not available here.

Share

COinS