Open Access Open Access  Restricted Access Subscription Access

Detection of Moving Object Using Morphological Filters


Affiliations
1 Department of Electronics & Communication Engineering, Bengal College of Engineering. & Technology, Durgapur, India
2 Department of Electronics & Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia,, India
 

In this paper, novel morphological filters are developed under the scope of traffic system in India is proposed. The algorithms of three filters are developed and implemented with their proper coding using Matlab (R2017a) software to detect the moving objects from CCTV video signal. For this aim, three filters are designed with gaining concepts of linear filters and also non-linear operators i.e., morphological operators. Noise reducing is also important to identify or detect a moving object. As the most of traffic videos contain background images and also different noise signals, it is necessary to minimize or to eliminate noise by subtracting background images from the images of traffic video. After detecting moving object using three morphological filters developed, PSNR and SNR values are also calculated for identified object to get the best filter designed. It is seen from the result, that moving object i.e., only white car detected after removing noise and applying median filter followed by morphological filter on background subtracted image, gives highest PSNR and SNR values.

Keywords

Morphological Filters, Binary Erosion and Dilation, Median Filter, Mean Filter, MATLAB Simulation.
User
Notifications
Font Size


  • Corso, J., Linear Filters and Image Processing, EECS 598-08 Lecture Notes, Foundations of Computer Vision, College of Engineering, Electrical Engineering & Computer Science, University of Michigan, Fall 2014.
  • Maragos, P., Morphological Signal and Image Processing, ©1999 by CRC Press LLC, Georgia Institute of Technology, 1996.
  • Gochoo, M. and Bayanduuren, D. et al., Design and Application of Novel Morphological Filter Used in Vehicle Detection, Proc 2016IEEE/ACIS 15th International Conference on Computer and Information (ICIS) (2016), Okayama, Japan, June 26-29, 2016, ISBN: 978-1-5090-0807-0, pp: 1-5, DOI:http://doi.ieeecomputersociety.org/10.1109/ICIS.2016.7550798, 2016.
  • Lee, J.S.J., and Haralick, R.M. et al., Morphologic Edge Detection, IEEE Trans. Rob. Autom., Vol. RA-3, pp. 142-156, Apr. 1987.
  • Loce, R .P. and Dougherty, E.R., Facilitation of Optimal Binary Morphological Filter Design via Structuring Element Libraries and Design Constraints, Optical Engineering, Vol. 31,pp. 1008-1025, May 1992.
  • Mara go s, P. an d Sch afe r, R.W., Morphological Filters. Part I: Their Set
  • Maragos , P. and Schafer, R . W. , Morphological Filters--Part II: Their Relations to Median, Order-Statistic, and Stack Filters, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 35 , Issue. 8, pp. 1170 - 1184, ISSN: 0096-3518 DOI: 10.1109/TASSP.1987.1165254, Aug 1987. Corrections, Vol.37, no.4, p.597, Apr. 1989.
  • Marag os, P. an d Sch afer, R.W., Morphological Systemsfor Multidimensional Signal Processing, Proc. IEEE, Vol. 78, pp. 690 –710, April 1990.
  • Nascimento, J. and Marques Jorge, Performance Evaluation of Object Detection Algorithms for Video Surveillence, IEEE Transactions on Multimedia, Volume: 8 , Issue: 4, DOI: 10.1109/TMM.2006.876287, pp. 761 - 774, Aug. 2006.
  • Chowdhury, F. A., and Shanchary, I. J. et al., Abandoned Object Detection with Video Surveillance, Thesis Paper, School of Computer Science and Engineering, BARK University, Fall 2014.
  • Herrero, E. and Orrite, C. et. al., VideoSensor for Detection and Tracking of Moving Objects. In: Perales F.J., Campilho A.J.C., de la Blanca N.P., Sanfeliu A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg, 2003.
  • Vipul, M.,Detecting moving Linear ShiftInvariant Filters., IEEEobjects in Video Frames, Thesis for Trans. Acoust. Speech, Signal Master of Technology, Department ofProcess., vol. 35, Issue 8, ISSN:0096Computer Science and Engineering 3518, p p . 11 5 3 - 11 6 9 , National Institute of Technology DOI:10.1109/TASSP. 1987.1165259, Rourkela, 2014.Aug. 1987
  • Kumar, P. and Singhal, A. et al., RealTime Moving Object Detection Algorithm on High-Resolution Videos Using GPUs, J RealTime Image Proc,DOI 10.1007/s11554-0120309-y, Springer-Verlag Berlin Heidelberg 2013.
  • Olugboja, A. and Wang, Z., Detection of Moving Objects Using Foreground Detector and Improved Morphological Filter, 3rd International Conference on Information Science and Control Engineering, 978-15090-2534-3 /16 © 2016 IEEE, IEEE Computer Society, pp. 329-333, 2016. DOI: 10.1109/ICISCE.2016.80.
  • Mahalingam, T. and Subramoniam, M., A Robust Single and Multiple Moving Object Detection, Tracking and classification, Applied Computing and Informatics (Article in Press),2018. https://doi.org/10.1016/ j.aci.2018.01.00.
  • Ray, K. S. and Chakraborty, S., An Efficient Approach for Object Detection and Tracking of Objects in a Video with Variable Background, arXiv:1706.02672v1 [cs.CV] 11 May 2017.
  • Risha K P and Chempak K. A." Novel Method of Detecting Moving Object in Video", International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST - 2015), Procedia Technology 24, pp. 1055 – 1060, 2016.
  • Oreifej, O. and L,i X. et al., Simultaneous Video Stabilization and Moving Object Detection in Turbulence, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 , Issue. 2 , pp. 450 - 462, ISSN: 13187927, DOI: 10.1109/TPAMI. 2012.97, IEEE Computer Society, Feb. 2013.
  • Yazdi, M. and Bouwmans, T., New Trends on Moving Object Detection in Video Images Captured by a moving Camera: A Survey, Computer Science Review, DOI: 10.1016/j.cosrev.2018.03.001, 2018
  • Yang, M., A Moving Objects Detection Algorithm in Video Sequence, ISBN: 978-14799-3903-9/14/$31.00 ©2014 IEEE, ICALIP2014, pp. 410-413, 2014.
  • Yang , Y. and Zhang, Q. et. al., Research Article Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model, Advances in Multimedia Vo l u m e 2 0 1 7 , Article ID 5179013 , https://doi.org/10.1155/2017/5179013, pp. 1-9, 2017.
  • Kalirajan, K. and Sudha, M., Research Article: Moving Object Detection for Video Surveillance. The Scientific World Journal, Vol. 2015, Article ID 907469, pp. 1-9, http://dx.doi.org/10.1155/2015/907469, 2015.

Abstract Views: 200

PDF Views: 30




  • Detection of Moving Object Using Morphological Filters

Abstract Views: 200  |  PDF Views: 30

Authors

Moti Kumar Jha
Department of Electronics & Communication Engineering, Bengal College of Engineering. & Technology, Durgapur, India
Bandana Barman
Department of Electronics & Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia,, India

Abstract


In this paper, novel morphological filters are developed under the scope of traffic system in India is proposed. The algorithms of three filters are developed and implemented with their proper coding using Matlab (R2017a) software to detect the moving objects from CCTV video signal. For this aim, three filters are designed with gaining concepts of linear filters and also non-linear operators i.e., morphological operators. Noise reducing is also important to identify or detect a moving object. As the most of traffic videos contain background images and also different noise signals, it is necessary to minimize or to eliminate noise by subtracting background images from the images of traffic video. After detecting moving object using three morphological filters developed, PSNR and SNR values are also calculated for identified object to get the best filter designed. It is seen from the result, that moving object i.e., only white car detected after removing noise and applying median filter followed by morphological filter on background subtracted image, gives highest PSNR and SNR values.

Keywords


Morphological Filters, Binary Erosion and Dilation, Median Filter, Mean Filter, MATLAB Simulation.

References





DOI: https://doi.org/10.21843/reas%2F2017%2F36-45%2F184052