Vehicle Speed Estimation Using Gaussian Mixture Model and Kalman Filter
Keywords:
Vehicle detection, Gaussian mixture model, Kalman filter, Perspective geometry model, Vehicle speed estimationAbstract
Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. To automate the process, an efficient and robust solution is needed. Vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. In this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. A background subtraction method based on the Gaussian Mixture Model (GMM) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. Next, the detected vehicles are tracked in each frame by using the Kalman Filter. Finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. The complete system is tested at our university campus and the results are promising.
References
[2] Sharma, V.; Nain, N.; Badal, T. (2015). A Survey on Moving Object Detection Methods in Video Surveillance, International Bulletin of Mathematical Research, 2(1), 209-218.
[3] Nayagam, G.; Ramar, K. (2015). A survey on Real time Object Detection and Tracking Algorithms, International Journal of Applied Engineering Research, 10(9), 8290-8297.
[4] Wicaksono, D.; Setiyono, B. (2017). Speed Estimation On Moving Vehicle Based On Digital Image Processing, International Journal of Computing Science and Applied Mathematics, 3(1), 21-26.doi: 10.12962/j24775401.v3i1.2117 https://doi.org/10.12962/j24775401.v3i1.2117
[5] Wang, J. (2016). Research of vehicle speed detection algorithm in video surveillance, International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, China, 349- 352,2016.doi: 10.1109/ICALIP.2016.7846573 https://doi.org/10.1109/ICALIP.2016.7846573
[6] Afifah, F.; Nasrin, S.; Mukit, A. (2018). Vehicle Speed Estimation using Image Processing, Journal of Advanced Research in Applied Mechanics, 48(1), 9-16.
[7] Gerát, J.; Sopiak, D.; Oravec, M. (2017). Vehicle Speed Detection from Camera Stream Using Image Processing Methods, in 59th International Symposium ELMAR, Zadar, Croatia, 201-204, 2017.doi: 10.23919/ELMAR.2017.8124468. https://doi.org/10.23919/ELMAR.2017.8124468
[8] Indrabayu; Bakti, R.; Sari Ar, I. (2016). Vehicle Detection and Tracking using Gaussian Mixture Model and Kalman Filter, in International Conference on Computational Intelligence and Cybernetics, Makassar, Indonesia, 115-119, 2016. doi: 10.1109/CyberneticsCom.2016.7892577. https://doi.org/10.1109/CyberneticsCom.2016.7892577
[9] Sina, I.; Wibisono, A.; Nurhadiyatna, A.; Hardjono, B.; Jatmiko, W.; Mursanto, P.(2013). Vehicle Counting and Speed Measurement Using Headlight Detection, " in International Conference on Advanced Computer Science and Information Systems (ICACSIS), Bali, Indonesia, 149-154, 2013. doi: 10.1109/ICACSIS.2013.6761567. https://doi.org/10.1109/ICACSIS.2013.6761567
[10] Bouziady, A.; Thami, R.; Ghogho, M.; Bourja, O.; Fkihi, S.(2018). Vehicle Speed Estimation Using Extracted SURF Features From Stereo Images, " in International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco,1-6, 2018.doi: 10.1109/ISACV.2018.8354040. https://doi.org/10.1109/ISACV.2018.8354040
[11] Hatwar, R.; Kamble, S.; Thakur, N.; Kakde, S. (2018). A Review on Moving Object Detection and Tracking Methods in Video, International Journal of Pure and Applied Mathematics, 118(16), 511-526.
[12] Stauffer, C.; Grimson, W. (1999). Adaptive background mixture models for real-time tracking Proceedings, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA, 246-252, 1999.doi: 10.1109/CVPR.1999.784637 https://doi.org/10.1109/CVPR.1999.784637
[13] Pong, P.; Bowden, R. (2001). An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection, in 2nd European Workshop on Advanced Video Based Surveillance Systems, Springer, Boston, 2001.
[14] Indrabayu; Basri; Achmad, A.; Nurtanio, I; Mayasari, F. (2015). Blob Modification In Counting Vehicles Using Gaussian Mixture Models Under Heavy Traffic, ARPN Journal of Engineering and Applied Sciences, 10(16), 7157-7163.
[15] Nurhadiyatna, A.; Hardjono, B.; Wibisono, A.; Sina, I.; Jatmiko, W.; Ma'Sum, A.; Mursanto, P. (2013). Improved vehicle speed estimation using gaussian mixture model and hole filling algorithm, International Conference on Advanced Computer Science and Information Systems (ICACSIS), Bali, 451-456.doi: 10.1109/ICACSIS.2013.6761617. https://doi.org/10.1109/ICACSIS.2013.6761617
[16] Balaji, S. R. ; Karthikeyan, S. (2017). A survey on moving object tracking using image processing, 11th international conference on intelligent systems and control (ISCO), Coimbatore, India, 469- 474.doi: 10.1109/ISCO.2017.7856037. https://doi.org/10.1109/ISCO.2017.7856037
[17] Gunjal, P. R.; Gunjal, B. R.; Shinde, H. A.; Vanam, S. M.; Aher, S. S. (2018). Moving object tracking using kalman filter, International Conference On Advances in Communication and Computing Technology (ICACCT), Sangamner, India, 544-547. doi:10.1109/ICACCT.2018.8529402. https://doi.org/10.1109/ICACCT.2018.8529402
[18] Bukey, C. M.; Kulkarni, S. V.; Chavan, R. A. (2017). Multi-object tracking using Kalman filter and particle filter, International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 1688-1692. doi:10.1109/ICPCSI.2017.8392001. https://doi.org/10.1109/ICPCSI.2017.8392001
[19] Indrabayu; Bakti, R.; Areni, I. (2018). A Modified Pinhole Camera Model For Automatic Speed Detection Of Diagonally Moving Vehicle, Journal of Engineering Science and Technology, 13(6), 1722-1734.
Additional Files
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
