International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Implementation on Surveillance Camera Optimum Angle Extraction using Polarizing Filter

  • Received : 2021.04.11
  • Accepted : 2021.04.17
  • Published : 2021.06.30
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Abstract

The surveillance camera market has developed and plays an important role in the field of video surveillance. However, in recent years, the identification of areas requiring surveillance has been limited by reflective light in the surveillance camera market. Cameras using polarization filters are being developed to reduce reflective light and facilitate identification. Programs are required to automatically adjust polarization filters. In this paper, we proposed an optimal angle extraction method from surveillance cameras using polarization filters through histogram analysis. First of all, transformed to grayscale to analyze the specifications of frames in multiple polarized angle images, reducing computational throughput. Then we generated and analyzed a histogram of the corresponding frame to extract the angle when the highlights are the fewest. Experiments with 0˚ and 90˚ showed high performance in extracting optimal angles. At this point, it is hoped this technology would be used for surveillance cameras in place like beach with a lot of reflective light.

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References

  1. L. B. Wolff and A. Andreou, "Polarization Camera Sensors", Image and Vision Computing Journal," Vol. 13, Issue 6, pp. 497-510, August 1995. DOI: https://doi.org/10.1016/0262-8856(95)94383-B
  2. Abd-Alameer, Shahad Ahmed, Hazim G. Daway, and Hayfa G. Rashid, "Quality of medical microscope Image at different lighting condition," IOP Conference Series: Materials Science and Engineering, Vol. 871, No. 1, pp.012072 June 2020. DOI: https://doi.org/10.1088/1757-899X/871/1/012072
  3. Wilfried M. O. and Ann M. R., "Automatic detection of regions of interest in complex video sequences", Human Vision and Electronic Imaging VI, Vol. 4299, pp. 361-372, June 2001. DOI: https://doi.org/10.1117/12.429506
  4. C. Saravanan, "Color Image to Grayscale Image Conversion," 2010 Second International Conference on Computer Engineering and Applications, Bali Island, Vol. 2, pp. 196-199, April 2010. DOI: https://doi.org/10.1109/ICCEA.2010.192
  5. L. Neumann, M. Cadik, and A. Nemcsics, "An efficient perception-based adaptive color to gray transformation," In Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging (Computational Aesthetics'07). Eurographics Association, Vol. 7, pp. 73-80, June 2007 DOI: https://dl.acm.org/doi/abs/10.5555/2381255.2381267
  6. L. B. Wolff, "Applications of polarization camera technology," in IEEE Expert, Vol. 10, No. 5, pp. 30-38, Oct 1995. DOI: https://doi.org/10.1109/64.464928
  7. F. Wang, S. Ainouz, C. Petitjean, A. Bensrhair, "Specularity removal: A global energy minimization approach based on polarization imaging," Computer Vision and Image Understanding, Vol. 158, pp. 31-39, May 2017. DOI: https://doi.org/10.1016/j.cviu.2017.03.003
  8. Soong-Der Chen and A. R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement," in IEEE Transactions on Consumer Electronics, Vol. 49, No. 4, pp. 1310-1319, Nov 2003. DOI: https://doi.org/10.1109/TCE.2003.1261234
  9. D. Coltuc, P. Bolon and J. -M. Chassery, "Exact histogram specification," in IEEE Transactions on Image Processing, Vol. 15, No. 5, pp. 1143-1152, May 2006. DOI: https://doi.org/10.1109/TIP.2005.864170
  10. Bin Xiao, Han Tang, Yanjun Jiang, Weisheng Li, Guoyin Wang, "Brightness and contrast controllable image enhancement based on histogram specification," Neurocomputing, Vol. 275, pp. 2798-2809, January 2018. DOI: https://doi.org/10.1016/j.neucom.2017.11.057
  11. Chaki J., Dey N., "Histogram-Based Image Color Features," Springer, pp. 29-41, June 2020. DOI: https://doi.org/10.1007/978-981-15-5761-3_2