Vehicle detection and tracking using corner feature points and artificial neural networks for a vision-based contactless apprehension system
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
Proceedings of Computing Conference 2017
Volume
2018-January
First Page
688
Last Page
691
Publication Date
1-8-2018
Abstract
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.
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Digitial Object Identifier (DOI)
10.1109/SAI.2017.8252170
Recommended Citation
Billones, R. C., Bandala, A. A., Sybingco, E., Gan Lim, L. A., Fillone, A. D., & Dadios, E. P. (2018). Vehicle detection and tracking using corner feature points and artificial neural networks for a vision-based contactless apprehension system. Proceedings of Computing Conference 2017, 2018-January, 688-691. https://doi.org/10.1109/SAI.2017.8252170
Disciplines
Mechanical Engineering | Transportation Engineering
Keywords
Intelligent transportation systems; Traffic monitoring—Equipment and supplies; Computer vision; Neural networks (Computer science)
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