Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation
© 2016 IEEE. Automatic tracking vehicle in urban traffic video surveillance is a challenging problem in computer vision. Although many issues have been solved, some are still unsolved, such as video surveillance problem of complex traffic intersection in congested condition. In this paper, we develo...
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2017
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th-cmuir.6653943832-405752017-09-28T04:10:15Z Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation Prommool P. Auephanwiriyakul S. Theera-Umpon N. © 2016 IEEE. Automatic tracking vehicle in urban traffic video surveillance is a challenging problem in computer vision. Although many issues have been solved, some are still unsolved, such as video surveillance problem of complex traffic intersection in congested condition. In this paper, we develop a vehicle counting system using a motion estimation with Taylor series approximation with embedded virtual entering and exiting boxes. The result shows that the system provides the counting success rate as high as 100% and the lowest counting rate is 14.29%. The mistakes are from the wrong direction prediction because of the very complex traffic condition. 2017-09-28T04:10:14Z 2017-09-28T04:10:14Z Conference Proceeding 2-s2.0-85018996613 10.1109/ICCSCE.2016.7893624 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018996613&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40575 |
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© 2016 IEEE. Automatic tracking vehicle in urban traffic video surveillance is a challenging problem in computer vision. Although many issues have been solved, some are still unsolved, such as video surveillance problem of complex traffic intersection in congested condition. In this paper, we develop a vehicle counting system using a motion estimation with Taylor series approximation with embedded virtual entering and exiting boxes. The result shows that the system provides the counting success rate as high as 100% and the lowest counting rate is 14.29%. The mistakes are from the wrong direction prediction because of the very complex traffic condition. |
| format |
Conference Proceeding |
| author |
Prommool P. Auephanwiriyakul S. Theera-Umpon N. |
| spellingShingle |
Prommool P. Auephanwiriyakul S. Theera-Umpon N. Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation |
| author_facet |
Prommool P. Auephanwiriyakul S. Theera-Umpon N. |
| author_sort |
Prommool P. |
| title |
Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation |
| title_short |
Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation |
| title_full |
Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation |
| title_fullStr |
Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation |
| title_full_unstemmed |
Vision-based automatic vehicle counting system using motion estimation with Taylor series approximation |
| title_sort |
vision-based automatic vehicle counting system using motion estimation with taylor series approximation |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018996613&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40575 |
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