Advertisement

Improving Image Quality for Detection of Illegally Parked Vehicle in No Parking Area

  • Rikita NagarEmail author
  • Hiteishi Diwanji
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)

Abstract

Nowadays, due to the increasing use of automobiles, as means of transportation people are facing heavy traffic problem in their day-to-day life. Most common cause of traffic is unauthorized parking on busy road. Generally, people do not find parking space in authorized parking lot or distance to authorized parking is too far, so they encouraged doing parking on roadside. This kind of illegal parking sometimes causes accidents. As high-quality video surveillance cost is reduced, detection of human activity and tracking becomes more practical. But still, detection of vehicles parked in no parking is a major task of the operators at surveillance office. So, there is need for such an automated traffic management system, which can detect vehicle parked in no parking. In the span of the most recent couple of years, numerous methods and framework have been proposed to detect illegally parked vehicle in no parking area. Although detection of an illegally parked vehicle in sudden light changing condition becomes more complex as video captured by the static camera is affected by low illumination or lighting condition. Due to the low contrast quality of the image is also reduced. So, vehicles parked in no parking area are not detected with higher precision and recall. In this work, we introduce steps which enhance image quality with respect to higher PSNR and low MSE for the purpose of detecting an illegally parked vehicle in no parking area.

Keywords

Vehicle detection Illegally parked vehicle Image quality Adaptive histogram equalization Histogram equalization Contrast enhancement PSNR MSE 

References

  1. 1.
    Pooja, Jatana, G.S.: Adaptive histogram equalization technique for enhancement of coloured image quality. Int. J. Latest Trends Eng. Technol. 8(2), 010–017.  https://doi.org/10.21172/1.82.002e. ISSN: 2278-621X
  2. 2.
  3. 3.
    Lee, J.T., Ryoo, M.S., Riley, M., Aggarwal, J.K.: Real-time illegal parking detection in outdoor environments using 1-D transformation. IEEE Trans. Circuits Syst. Video Tech. 19(7) (2009)Google Scholar
  4. 4.
    Xie, X., Wang, C., Chen, S., Shi, G., Zhao, Z.: Time illegal parking detection system based on deep learning. In: Proceedings of the 2017 International Conference on Deep Learning Technologies, pp. 23–27Google Scholar
  5. 5.
    Filonenko, A., Jo, K.H.: Illegally parked vehicle detection using adaptive dual background model. In: Proceedings of IEEE Industrial Electronic Society Conference (IECON). Yokohama, Nov 2015Google Scholar
  6. 6.
    Filonenko, A., Jo, K.H.: Detecting illegally parked vehicle based on cumulative dual foreground difference. In: IEEE 14th International Conference on Industrial Informatics (2016)Google Scholar
  7. 7.
    Hassan, W., Birch, P., Young, R., Chatwin, C.: Real Time occlusion tolerant detection of illegally parked vehicles. Int. J. Control Autom. Syst. 10(5), 972982 (2012)CrossRefGoogle Scholar
  8. 8.
    Tiwari, M., Rakesh, S.: A review of detection and tracking of object from image and video sequences. Int. J. Comput. Intell. Res. 13(5), 745–765 (2017). ISSN 0973-1873. Research India PublicationsGoogle Scholar
  9. 9.
    Karasulu, B., Korukoglu, S.: Moving Object Detection and Tracking in Videos, XV, 76 p. 11 illus., softcover. ISBN 978-1-4614-6533-1. http://www.springer.com/978-1-4614-6533-1 (2013)
  10. 10.
    Shaikh, S.H., et al.: Moving Object Detection Using Background Subtraction, SpringerBriefs in Computer Science.  https://doi.org/10.1007/978-3-319-07386-6_2, © The Author(s) 2014
  11. 11.
    Nurhadiyatna1, A., Jatmiko1, W., Hardjono1, B.: Background subtraction using gaussian mixture model enhanced by hole filling algorithm (GMMHF). In: 2013 IEEE International Conference on Systems, Man, and CyberneticsGoogle Scholar
  12. 12.
    Piccardi, M.: Background subtraction techniques: a review. In: 2004 IEEE International Conference on Systems, Man and CyberneticsGoogle Scholar
  13. 13.
    Panahi, S., Sheikhi, S., Hadadan, S., Gheissari, N.: Evaluation of background subtraction methods. 978-0-7695-3456-5/08, © 2008. IEEE  https://doi.org/10.1109/dicta.2008.52
  14. 14.
    Alandkar, L., Gengaj, S.R.: Dealing background issues in object detection using GMM: a survey. Int. J. Comput. Appl. 150(5), 0975–8887 (2016)Google Scholar
  15. 15.
    Ma, Y.L., Chang, Z.C.: Moving vehicles detection based on improved gaussian mixture model. In: International Conference of Electrical, Automation and Mechanical Engineering (EAME) (2015)Google Scholar
  16. 16.
    Chavan, R., Gangej, S.R.: Multiple object detection using GMM technique and tracking using Kalman filter. Int. J. Comput. Appl. 172(3), 0975–8887 (2017)Google Scholar
  17. 17.
    Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: Feature similarity index for image quality assessment. In: IEEE Transactions on Image Processing 20(8) (2011)Google Scholar
  18. 18.
    Zhou, C., Yang, X., Zhang, B., Lin, K., Xu, D., Guo, Q., Sun, C.: An adaptive image enhancement method for a recirculating aquaculture system. Sci. Rep. 7, 6243. Published online 24 July 2017.  https://doi.org/10.1038/s41598-017-06538-9 (2017)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Government Polytechnic for GirlsAhmedabadIndia
  2. 2.L. D. College of EngineeringAhmedabadIndia

Personalised recommendations