MULTI VEHICLE SPEED DETECTION USING EUCLIDEAN DISTANCE BASED ON VIDEO PROCESSING
DOI:
https://doi.org/10.47839/ijc.18.4.1613Keywords:
Smart City, Vehicle speed estimation, Gaussian Mixture Model, Region of Interest, Euclidean Distance.Abstract
One component of smart city is smart transportation, known as Intelligent Transportation Systems (ITS). In this study, we discuss the estimation of moving vehicle speed based on video processing using the Euclidean Distance method. In this study, we examine the effect of camera angles on the video acquisition to speed estimation accuracy. In addition, Region of Interest (ROI) will be designed into three parts to determine which area is the most appropriate to be chosen, so that the estimated vehicle speed will be better. These approaches have never been studied by previous researchers. The separation between the background and foreground is conducted using Gaussian Mixture Models method. By comparing the displacement distance and the number of frames per second (fps), we obtain speed estimate for each vehicle. According to the experimental results, our system can estimate the speed of the vehicle with an accuracy of 99.38%.References
V. Albino, U. Berardi, and R. M. Dangelico, “Smart cities: definition, deminsion, and performance,” J. Urban Technol., vol. 22, pp. 3–21, 2015.
M. Mirboland and K. Smarsly, “A semantic model of intelligent transportation systems,” no. 2011, pp. 1–10, 2015.
M. A. Khan, S. Abbas, Z. Hasan, and A. Fatima, “Intelligent Transportation System ( ITS ) for Smart-Cities using Mamdani Fuzzy Inference System,” no. January, 2018.
T. Kumar and D. S. Kushwaha, “An Efficient Approach for Detection and Speed Estimation of Moving Vehicles,” Procedia Comput. Sci., vol. 89, pp. 726–731, 2016.
S. Indu, M. Gupta, and P. A. Bhattacharyya, “Vehicle Tracking and Speed Estimation using Optical Flow Method,” Int. J. Eng. Sci. Technol., vol. 3, no. 1, pp. 429–434, 2011.
J. Lan, J. Li, G. Hu, B. Ran, and L. Wang, “Vehicle speed measurement based on gray constraint optical flow algorithm,” Opt. - Int. J. Light Electron Opt., vol. 125, no. 1, pp. 289–295, Jan. 2014.
R. Ke, S. Kim, Z. Li, and Y. Wang, “Motion-vector clustering for traffic speed detection from UAV video,” 2015 IEEE 1st Int. Smart Cities Conf. ISC2 2015, no. October, 2015.
S. S. S. Ranjit, S. A. Anas, S. K. Subramaniam, K. C. Lim, A. F. I. Fayeez, and A. R. Amirah, “Real-Time Vehicle Speed Detection Algorithm using Motion Vector Technique,” Proc. Int. Adv. Electr. Electron. 2012, pp. 67–71, 2012.
P. P. S. S and C. Manisha, “Vehicle Detection and Tracking using the Optical Flow and Background Subtraction.”
D. Woo and A. N. Hong, “An Integrated Solution to Motion Tracking of Moving Vehicles Using Optical Flow,” vol. 1, no. 1, 2014.
B. Setiyono, D.R. Sulistyaningrum, Soetrisno, F. Fajriyah, and D. W. Wicaksono, “Vehicle speed detection based on Gaussian mixture model using sequential of images,” vol. 890, no. 1, pp. 0–6, 2017.
T. Huang, H. Peng, and K. Zhang, “Model Selection for Gaussian Mixture Models,” arXiv:1301.3558v1, pp. 1–27, 2013.
D. Reynolds, “Gaussian Mixture Models,” Encycl. Biometric Recognit., vol. 31, no. 2, pp. 1047–64, 2008.
Y. Li, Z. Li, H. Tian, and Y. Wang, “Vehicle Detecting and Shadow Removing Based on Edged Mixture Gaussian Model,” IFAC Proc. Vol., vol. 44, no. 1, pp. 800–805, Jan. 2011.
M. K. Sarker, C. Weihua, and M. K. Song, “Detection and Recognition of Illegally Parked Vehicles Based on an Adaptive Gaussian Mixture Model and a Seed Fill Algorithm,” vol. 13, no. 3, pp. 197–204, 2015.
D. H. H. Santosh, P. Venkatesh, P. Poornesh, L. N. Rao, and N. A. Kumar, “Tracking Multiple Moving Objects Using Gaussian Mixture Model,” no. 2, pp. 114–119, 2013.
A. Gholami Rad, A. Dehghani, and M. Rehan Karim, “Vehicle speed detection in video image sequences using CVS method,” Int. J. Phys. Sci., vol. 5, no. 17, pp. 2555–2563, 2010.
W. Wu, J. Shao, and W. Guo, “Moving-object Detection Based on Shadow Removal and Prospect,” Int. Conf. Artif. Intell. Soft Comput., vol. 12, no. 2, pp. 369–374, 2012.
B. Setiyono, D. R. Sulistyaningrum, and H. Al-habib, “Improvement of sub region matching illumination transfer in hybrid shadow removal method for moving vehicle video,” Int. J. Eng. Technol., vol. 7, no. 4, pp. 2515–2520, 2018.
A. Prati, I. Miki, M. M. Trivedi, and R. Cucchiara, “Detecting Moving Shadows :,” no. 858, pp. 1–15.
S. Murali, V. K. Govindan, and S. Kalady, “A Survey on Shadow Removal Techniques for Single Image,” Int. J. Image, Graph. Signal Process., vol. 8, no. 12, pp. 38–46, 2016.
H. Prajapati, S. Longowal, B. Singh, and S. Longowal, “An Approach for Shadow Removal in Moving Object,” Int. J. Comput. Sci. Inf. Secur., vol. 14, no. 8, pp. 781–790, 2016.
Y. W. Shou, C. T. Lin, C. T. Yang, and T. K. Shen, “An efficient and robust moving shadow removal algorithm and its applications in ITS,” EURASIP J. Adv. Signal Process., vol. 2010, 2010.
A.G. Mangala and R. Balasubramani, “A Review On Vehicle Speed Detection Using Image Processing,” Int. J. Curr. Eng. Sci. Res., vol. 4, no. 11, pp. 23–28, 2017.
K. Kobayashi, K. C. Cheok, and K. Watanabe, “Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter,” no. March, pp. 3086–3090, 2005.
H. Pazhoumand-Dar, “Object Speed Estimation by using Fuzzy Set,” J. World Acad. …, vol. 4, no. 4, pp. 241–244, 2010.
J. Pouramini and A. Saeedi, “Fuzzy Model Identification for Intelligent Control of a Vehicle Speed Limit,” J. Math. Comput. Sci., vol. 2, no. 2, pp. 337–347, 2011.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.