Vehicle detection and tracking using corner feature points and artificial neural networks for a vision-based contactless apprehension system

• Main Authors: Billones, Robert Kerwin C., Bandala, Argel A., Sybingco, Edwin, Gan Lim, Laurence A., Fillone, Alexis D., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2018
• Subjects: Intelligent transportation systems; Traffic monitoring—Equipment and supplies; Computer vision; Neural networks (Computer science); Mechanical Engineering; Transportation Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1931
• Institution: De La Salle University

Blocked intersections have been a contributing factor in the city-wide traffic congestion, especially in metropolitan cities. This research study aims to develop a better traffic violations management system in city-road intersections by using a machine vision system that automatically identifies and tags traffic violations committed in an intersection. The proposed system have three main sub-systems which are the video capture, video analysis, and output sub-systems. This study presents the development and results of a vehicle detection and tracking system using corner feature point detection and artificial neural networks for the vision-based contactless traffic violations apprehension system. This detection and tracking system serves as the front-end processing in the video analysis sub-system. Experiments were conducted for different corner feature-points detection algorithm: Harris, Shi-Tomasi, and Features from Accelerated Segment Test (FAST). The results showed that in the testing phase Harris-ANN have 89.09% accuracy, Shi-Tomasi-ANN have 88.48%, and FAST-ANN have 90.30% accuracy. © 2017 IEEE.