This research initiatives with the primary purposes of detecting, tracking and classifying automobiles; however, it is also applied to driver behavior detection, lane recognition and other coherent applications. This framework is used in a variety of domains including public safety, accident revealing, automobiles detection, parking lots, theft finding and human identity recognition. Due to a growth in the number of automobiles; highways and roadways are becoming overcrowded. As a result, the frequency of the accidents and violations of traffic laws has skyrocketed. For this reason, vehicle detection and counting become essential to the traffic management. This study ensures the balance traffic system by detecting and counting the vehicles through real time video capturing. The proposed model is mostly based on a video-based technique for vehicle recognition and counting that employs the Python programming language OpenCV. The code editor “Visual Studio Code” is used to create and implement the framework for the empirical part. Moreover, to achieve real-time instinctive automobiles counting and detecting, software is combined with Intel's OpenCV video streaming system. This structure can quickly recognize and track automobiles as well as assists in the counting of the objects. This research can also be used to locate criminals on the road and traffic rule violators so that traffic controllers can take immediate action.
| Published in | Science Frontiers (Volume 2, Issue 4) |
| DOI | 10.11648/j.sf.20210204.13 |
| Page(s) | 61-66 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Traffic Management, OpenCV, Subtractor MOG Algorithm, Object Detection, Vehicle Counting, Vehicle Classification
| [1] | Nilesh J Uke and Ravindra C Thool “Moving Vehicle Detection for Measuring Traffic Count Using OpenCV” Journal of Automation and Control Engineering Vol. 1, No. 4, December 2013. |
| [2] | Karthik Srivathsa D S and Kamalraj R “VEHICLE DETECTION AND COUNTING OF A VEHICLE USING OPENCV” International Research Journal of Modernization in Engineering Technology and Science Volume: 03/ Issue: 05/ May-2021. |
| [3] | Sowmya Kini Ma and et. al., “Real Time Moving Vehicle Congestion Detection and Tracking using OpenCV” Turkish Journal of Computer and Mathematics Education Vol. 12 No. 10 (2021), 273-279. |
| [4] | D Agustiani1 and et. al., ‘OpenCV and Machine Learning Implementation for the Vehicles Classification and Calculation in the Parking Tax Monitoring System at the Bantul Regency Regional Financial and Asset Agency (BKAD)”, Journal of Physics Conference Series, March 2021. |
| [5] | Sheeraz Memon and et. al., “A Video based Vehicle Detection, Counting and Classification System” International Journal of Image, Graphics and Signal Processing 10 (9): 34-41, September 2018. |
| [6] | W. J. Wang and M. Gao, “Vehicle detection and counting in traffic video based on OpenCV,” Applied Mechanics and Materials (Volumes 361-363), pp. 2232–2235, 2013. |
| [7] | G. Meena and et. al., “Traffic Prediction for Intelligent Transportation System using Machine Learning,” 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), 2020, no. February, pp. 145–148, 2020. |
| [8] | D. Li, B. Liang, and W. Zhang, “Real-time moving vehicle detection, tracking, and counting system implemented with OpenCV,” 4th IEEE International Conference on Information Science and Technology, pp. 631–634, April, 2014. |
| [9] | E. Bas, A. M. Tekalp and F. S. Salman, “Automatic vehicle counting from video for traffic flow analysis”, IEEE Intelligent Vehicles Symposium, 2007. |
| [10] | H. Rabiu, “Vehicle detection and classification for cluttered urban intersection”, International Journal of Computer Science, Engineering and Applications, vol 3, no 1, p. 37, 2013. |
| [11] | M. Seki, H. Fujiwara and K. Sumi, “A robust background subtraction method for changing background”, Fifth IEEE Workshop on Applications of Computer Vision, Dec., 2000. |
| [12] | K. Wu and et al., “Overview of video-based vehicle detection technologies”, 6th International Conference on Computer Science & Education (ICCSE), August, 2011. |
APA Style
Ohidujjaman, Fahima Siddika, Shahriar Hossain, Taskinmostofa Azam, Shoyaib Mahmud, et al. (2021). Traffic Management System Through Vehicle Detection and Counting. Science Frontiers, 2(4), 61-66. https://doi.org/10.11648/j.sf.20210204.13
ACS Style
Ohidujjaman; Fahima Siddika; Shahriar Hossain; Taskinmostofa Azam; Shoyaib Mahmud, et al. Traffic Management System Through Vehicle Detection and Counting. Sci. Front. 2021, 2(4), 61-66. doi: 10.11648/j.sf.20210204.13
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Download@article{10.11648/j.sf.20210204.13,
author = {Ohidujjaman and Fahima Siddika and Shahriar Hossain and Taskinmostofa Azam and Shoyaib Mahmud and Shammir Hossain and Jakia Rawnak Jahan and Mohammad Monirul Islam},
title = {Traffic Management System Through Vehicle Detection and Counting},
journal = {Science Frontiers},
volume = {2},
number = {4},
pages = {61-66},
doi = {10.11648/j.sf.20210204.13},
url = {https://doi.org/10.11648/j.sf.20210204.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sf.20210204.13},
abstract = {This research initiatives with the primary purposes of detecting, tracking and classifying automobiles; however, it is also applied to driver behavior detection, lane recognition and other coherent applications. This framework is used in a variety of domains including public safety, accident revealing, automobiles detection, parking lots, theft finding and human identity recognition. Due to a growth in the number of automobiles; highways and roadways are becoming overcrowded. As a result, the frequency of the accidents and violations of traffic laws has skyrocketed. For this reason, vehicle detection and counting become essential to the traffic management. This study ensures the balance traffic system by detecting and counting the vehicles through real time video capturing. The proposed model is mostly based on a video-based technique for vehicle recognition and counting that employs the Python programming language OpenCV. The code editor “Visual Studio Code” is used to create and implement the framework for the empirical part. Moreover, to achieve real-time instinctive automobiles counting and detecting, software is combined with Intel's OpenCV video streaming system. This structure can quickly recognize and track automobiles as well as assists in the counting of the objects. This research can also be used to locate criminals on the road and traffic rule violators so that traffic controllers can take immediate action.},
year = {2021}
}
TY - JOUR T1 - Traffic Management System Through Vehicle Detection and Counting AU - Ohidujjaman AU - Fahima Siddika AU - Shahriar Hossain AU - Taskinmostofa Azam AU - Shoyaib Mahmud AU - Shammir Hossain AU - Jakia Rawnak Jahan AU - Mohammad Monirul Islam Y1 - 2021/12/29 PY - 2021 N1 - https://doi.org/10.11648/j.sf.20210204.13 DO - 10.11648/j.sf.20210204.13 T2 - Science Frontiers JF - Science Frontiers JO - Science Frontiers SP - 61 EP - 66 PB - Science Publishing Group SN - 2994-7030 UR - https://doi.org/10.11648/j.sf.20210204.13 AB - This research initiatives with the primary purposes of detecting, tracking and classifying automobiles; however, it is also applied to driver behavior detection, lane recognition and other coherent applications. This framework is used in a variety of domains including public safety, accident revealing, automobiles detection, parking lots, theft finding and human identity recognition. Due to a growth in the number of automobiles; highways and roadways are becoming overcrowded. As a result, the frequency of the accidents and violations of traffic laws has skyrocketed. For this reason, vehicle detection and counting become essential to the traffic management. This study ensures the balance traffic system by detecting and counting the vehicles through real time video capturing. The proposed model is mostly based on a video-based technique for vehicle recognition and counting that employs the Python programming language OpenCV. The code editor “Visual Studio Code” is used to create and implement the framework for the empirical part. Moreover, to achieve real-time instinctive automobiles counting and detecting, software is combined with Intel's OpenCV video streaming system. This structure can quickly recognize and track automobiles as well as assists in the counting of the objects. This research can also be used to locate criminals on the road and traffic rule violators so that traffic controllers can take immediate action. VL - 2 IS - 4 ER -
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