Classification algorithm of parking space images based on a histogram of oriented gradients and support vector machines
P.V. Yarashevich, R.P. Bohush

 

Polotsk State University, Polotsk, Belarus

Full text of article: Russian language.

 PDF

Abstract:
In this paper, a classification algorithm of parking space images is proposed to improve the accuracy of parking space classification, which can be used in smart parking management systems based on video surveillance. The descriptors of a parking space image are formed on the basis of a histogram of oriented gradients by performing the following steps: computation of vertical and horizontal gradients of the original parking space image, computation of the modulus of the gradient and orientation vectors, the gradients are then accumulated into separate cells according to their orientation, the cells are united into blocks, and the orientations of block's cells are normalized. A support vector machine is used to classify the descriptors of the parking space. The purpose of the research was to determine the most efficient parameters of the parking space descriptor and a kernel function. The paper presents the results of experiments.

Keywords:
machine vision, image analysis, pattern recognition.

Citation:
Yarashevich PV, Bohush RP. Classification algorithm of parking space images based on a histogram of oriented gradients and support vector machines. Computer Optics 2017; 41(1): 110-117. DOI: 10.18287/2412-6179-2017-41-1-110-117.

References:

  1. Ngan K, Li H. Video Segmentation and Its Applications. New York, Dordrecht, Heidelberg, London: Springer; 2011. DOI: 10.1007/978-1-4419-94182-0.
  2. Brovko N, Bogush R, Ablameyko S. Smoke detection algorithm for intelligent video surveillance system. Computer Science Journal of Moldova 2013; 21(1)(61): 142-156.
  3. Vasin NN, Diyazitdinov RR. A machine vision system for inspection of railway track. Computer optics 2016; 40(3): 410-415. DOI: 10.18287/2412-6179-2016-40-3-410-415.
  4. Idris MYI, Leng YY, Tamil EM, Noor NM, Razak Z. Car park system: A review of smart parking system and its technology. Information Technology Journal 2009; 8(2): 101-113. DOI:10.3923/itj.2009.101.113.
  5. True N. Vacant parking space detection in static images. Technical Report. San Diego: University of California; 2007.
  6. Bong DBL, Tng KC, Rajaee N. Car-park occupancy information system. Third Real-Time Technology and applications symposium 2006.
  7. Sastre RJL, Jimenez PG, Acevedo FJ, Bascon SM. Computer algebra algorithms applied to computer vision in a parking management system. IEEE ISIE 2007: 1675-1680. DOI: 10.1109/ISIE.2007.4374856.
  8. Almeida P, Oliveira LS, Silva E, Britto A, Koerich AL. Parking space detection using textural descriptors. IEEE SMC 2013: 3603-3608. DOI:10.1109/SMC.2013.614.
  9. Huang C-C, Tai Y-S, Wang S-J. Vacant parking space detection based on plane-based bayesian hierarchical framework. IEEE Transactions on Circuits and Systems for Video Technology 2013; 23(9): 1598-1610. DOI: 10.1109/TCSVT.2013.2254961.
  10. Jermsurawong J, Ahsan MU, Haidar A, Dong H, Mavridis N. Car parking vacancy detection and its application in 24-hour statistical analysis. 10th International Conference on Frontiers of Information Technology 2012: 84-90. DOI: 10.1109/FIT.2012.24.
  11. Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection. CVPR’05 2005; 1: 886-893. DOI: 10.1109/CVPR.2005.177.
  12. Vapnic VN, Chervonenkis AYa. Theory of pattern recognition [In Russian]. Moscow: “Nauka” Publisher; 1974.
  13. PKLot – A robust dataset for parking lot classification. Source: áhttp://web.inf.ufpr.br/vri/news/parking-lot-databaseñ.
  14. Huang C-C, Dai Y-S, Wang S-J. A surface-based vacant space detection for an intelligent parking lot. Proceedings of 12th international conference on ITS telecommunications 2012: 284-288. DOI: 10.1109/ITST.2012.6425183.
  15. Huang C-C, Vu HT, Chen Y-R. A multiclass boosting approach for integrating weal classifiers in parking space detection. ICCE TW 2015: 314-315. DOI: 10.1109/ICCE-TW.2015.7216918.
  16. Fusek R, Mozdren K, Šurkala M, Sojka E. AdaBoost for parking lot occupation detection. CORES 2013: 681-690. DOI: 10.1007/978-3-319-00969-8_67.
  17. Tschentscher M, Koch C, König M, Salmen J, Schlipsing M. Scalable real-time parking lot classification an evaluation of image features and supervised learning algorithms. IJCNN 2015: 1-8. DOI: 10.1109/IJCNN.2015.7280319.
  18. Baroffio L, Bondi L, Cesana M, Redondi A, Tagliasacchi M. A visual sensor network for parking lot occupancy detection in Smart Cities. WF-IoT 2015: 745-750. DOI: 10.1109/WF-IoT.2015.7389147.
© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: journal@computeroptics.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20