“Mass Centre” Vectorization Algorithm for Vehicle’s Counting Portable Video System

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Abstract

Vehicle counting is one of the most basic challenges during the development and establishment of Intelligent Transport Systems (ITS). The main reason for vehicle counting is the necessity of monitoring and maintaining the transport infrastructure, preventing different kind of faults such as traffic jams. The main applied solution to this problem is video surveillance, which is presented by different kind of systems. Some of these systems use a network of static traffic cameras, expensive for establish and maintain, or mobile units, fast for redeployment, but fewer in diversity.

In this paper, one particular concept of a low-cost mobile vehicle counting system is investigated, which uses an object detection method based on calculating “mass centre” of detected features of possible object. A hypothesis of improvement of the basic algorithm was formulated and a modification was proposed. In order to prove the hypothesis, both basic and modified algorithms were tested and evaluated.

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  • 1. Aslani S. and Mahdavi-Nasab H. (2013) Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance. In: World Academy of Science Engineering and Technology. International Journal of Electrical Computer Energetic Electronic and Communication Engineering 7(9) 1252-1256 DOI: scholar.waset.org/1999.5/17157.

  • 2. Bouguet J.-Y. (2000) Pyramidal Implementation of the Lucas Kanade Feature Tracker. Description of the algorithm. Intel Corporation Microprocessor Research Labs.

  • 3. Cao Y. Lei Z. Huang X. Zhang Z. and Zhong T. (2012) A Vehicle Detection Algorithm Based on Compressive Sensing and Background Subtraction. In: AASRI Procedia 1 480-485 DOI: 10.1016/j.aasri.2012.06.075.

  • 4. Fischer Y. and Beyerer J. (2012) A top-down-view on intelligent surveillance systems. In: Proceedings of the Seventh International Conference on Systems (ICONS) February 29 - March 5 2012 Saint Gilles Reunion Island pp. 43–48.

  • 5. Grakovski A. and Murza A. (2010) Development of Segmentation Method Based on “Mass Centre” Approach for Video Surveillance Data of Transport Vehicles Flow. In: Transport and Telecommunication 11(2) 18-29.

  • 6. Harris C. and Stephens M. (1988). A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference August 31 – September 2 1988 University of Manchester Manchester United Kingdom pp. 147–151.

  • 7. Kadikis R. and Freivalds K. (2013) Vehicle classification in video using virtual detection lines. In: Proceedings of SPIE. Sixth International Conference on Machine Vision (ICMV 2013) Volume 9067 DOI: 10.1117/12.2051028.

  • 8. Lucas B. and Kanade T. (1981) An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of the 7th international joint conference on Artificial intelligence (IJCAI’81) August 24-28 1981 University of British Columbia Vancouver B.C. Canada Volume 2 pp. 674-679.

  • 9. Shi J. and Tomasi C. (1994) Good Features to Track-In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’94) June 1994 Seattle USA pp. 593-600.

  • 10. Tomasi C. and Kanade T. (1991) Detection and Tracking of Point Features. Carnegie Mellon University Technical Report CMU-CS-91-132 April 1991.

  • 11. Tuytelaars T. and Mikolajczyk K. (2007) Local Invariant Feature Detectors: A Survey. In: Foundations and Trends in Computer Graphics and Vision 3(3) 177–280 DOI: 10.1561/0600000017.

  • 12. Wang H. and Zhang H. (2014) A Hybrid Method of Vehicle Detection based on Computer Vision for Intelligent Transportation System. In: International Journal of Ubiquitous Engineering 9(6) 105-118 DOI: dx.doi.org/10.14257/ijmue.2014.9.6.11.

  • 13. Zhang G. Avery R. and Wang Y. (2007) Video-Based Vehicle Detection and Classification System for Real-Time Traffic Data Collection Using Uncalibrated Video Cameras. In: Transportation Research Record: Journal of the Transportation Research Board 1993 138-147 DOI: http://dx.doi.org/10.3141/1993-19.

  • 14. Buch N.E. (2010) Classification of vehicles for urban traffic scenes. PhD thesis. Kingston University viewed 2 November 2016 http://www.bmva.org/thesis-archive/2010/2010-buch.pdf.

  • 15. Gillman O. (2014) Councils will be ordered to come clean about how much they make from parking fines. October 4 2014. Daily Mail Online viewed 2 November 2016 http://www.dailymail.co.uk/news/article-2780616/Councils-ordered-come-clean-make-parking-fines.html#ixzz4C0sODcKY.

  • 16. Vitronic n.d. Police Scan Remote Camera. Product Update viewed 2 November 2016 https://www.vitronic.com/traffic-technology/applications/traffic-enforcement/speed-enforcement/poliscan-speed-mobile.html.

  • 17. Vysionics n.d. SPECS viewed 2 November 2016 http://www.vysionics.com/product/specs.

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Cite Score 2018: 1.19

SCImago Journal Rank (SJR) 2018: 0.251
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