KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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An Improved ViBe Algorithm of Moving Target Extraction for Night Infrared Surveillance Video

  • Feng, Zhiqiang (School of Automation and Information Engineering, Sichuan University of Science and Engineering) ;
  • Wang, Xiaogang (School of Automation and Information Engineering, Sichuan University of Science and Engineering) ;
  • Yang, Zhongfan (School of Automation and Information Engineering, Sichuan University of Science and Engineering) ;
  • Guo, Shaojie (School of Automation and Information Engineering, Sichuan University of Science and Engineering) ;
  • Xiong, Xingzhong (School of Automation and Information Engineering, Sichuan University of Science and Engineering)
  • Received : 2021.08.06
  • Accepted : 2021.11.01
  • Published : 2021.12.31
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Abstract

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

Keywords

References

  1. J. Lipton, H. Fujiyoshi, R. S. Patil, "Movingtarget classification and tracking from real-time video," in Proc. of the 4th IEEE workshop on applications of computer vision WACV'98 (Cat. No.98EX201), pp. 8-14, 1998.
  2. D. Sun, S. Roth, M. J. Black, "Secrets of optical flow estimation and their principles," in Proc. of the IEEE computer society conference on computer vision and pattern, pp. 2432-2439, 2010.
  3. A. F. Bobick, J. W. Davis, "The recognition of human movement using temporal templates," IEEE Transactions on pattern analysis and machine intelligence, vol. 23, no. 3, pp. 257 - 267, Mar. 2001. https://doi.org/10.1109/34.910878
  4. I. Haritaoglu, D. Harwood, L. S. Davis, "W/sup 4: real-time surveillance of people and their activities," IEEE Transactions on pattern analysis and machine intelligence, vol. 22, no. 8, pp. 809 - 830, Aug. 2000. https://doi.org/10.1109/34.868683
  5. O. Barnich, M. V. Droogenbroeck, "ViBe:A Universal Background Subtraction Algorithm for Video Sequences," IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1709-1724, Jun. 2011. https://doi.org/10.1109/TIP.2010.2101613
  6. W. Huang, L. Liu, C. Yue, et al., "The moving target detection algorithm based on the improved visual background extraction," Infrared Physics & Technology, vol. 71, pp. 518-525, Jul. 2015. https://doi.org/10.1016/j.infrared.2015.06.011
  7. M. Shaowen, D. Xinpu, W. Shuai, et al., "Moving object detection algorithm based on improved visual background extractor," Acta Optica Sinica, vol. 36, no. 6, pp. 204-213, Jan. 2016.
  8. W. Yuanbin and R. Jieying, "An Improved Vibe Based on Gaussian Pyramid," in Proc. of the 4th International Conference on Control and Robotics Engineering (ICCRE), pp. 105-109, 2019.
  9. Y. Yue, D. Xu, Z. Qian, H. Shi, et al., "Ant_ViBe: Improved ViBe Algorithm Based on Ant Colony Clustering under Dynamic Background," Mathematicical Problems in Engineering, vol. 2020, pp.1-13, Aug. 2020.
  10. L. Liu, G. Chai, Z. Qu, "Moving target detection based on improved ghost suppression and adaptive visual background extraction," Journal of Central South University, vol. 28, no. 3, pp.747-759, Apr. 2021. https://doi.org/10.1007/s11771-021-4642-9
  11. X. Liu, T. Zhong, D. Fu, "Robust Compositional Method for Background Subtraction," in Proc. of the 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1419-1424, 2012.
  12. H. Guang, J. Wang, C. Xi, "Improved visual background extractor using an adaptive distance threshold," Journal of Electronic Imaging, vol. 23, no. 3, pp. 036005, Nov. 2014.
  13. H. Wang, Q. Wang, Y. Li, et al., "An illumination-robust algorithm based on visual background extractor for moving object detection," in Proc.of the 10th Asian Control Conference (ASCC), pp. 1-6, 2015.
  14. Z. Peng, F. Chang, W. Dong, "Vibe Motion Target Detection Algorithm Based on Lab Color Space," in Proc.of the Chinese Conference on Image and Graphics Technologies, pp 45-54, 2015.
  15. J. Cao, S. Zhao, X. Sun, et al., "Algorithm of moving object detection with illumination robustness based on confidence," in Proc.of the IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1058-1062, 2017.
  16. Q. Zhang, W. Lu, C. Huang, et al., "An adaptive vibe algorithm based on dispersion coefficient and spatial consistency factor," Automatic Control and Computer Sciences, vol. 54, no. 1, pp. 80-88, Mar, 2020. https://doi.org/10.3103/s0146411620010101
  17. Y. Zhang, W. Zheng, K. Leng, et al., "Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes," IEEE Access, vol. 8, pp. 130367-130378, 2020. https://doi.org/10.1109/access.2020.3009104
  18. X. Hou, L. Zhang, "Saliency detection: A spectral residual approach," in Proc.of the 2007 IEEE Conference on computer vision and pattern recognition, pp. 1-8, 2007.
  19. Y. Ren, J. Zhou, Z. Wang and Y. Yan, "An Improved Saliency Detection for Different Light Conditions," KSII Transactions on Internet and Information Systems, vol. 9, no. 3, pp. 1155-1172, 2015. https://doi.org/10.3837/tiis.2015.03.018
  20. F, Kallel, A. B. Hamida, "A new adaptive gamma correction based algorithm using DWT-SVD for non-contrast CT image enhancement," IEEE transactions on nanobioscience, vol. 16, no. 8, pp.666-675, Nov. 2017. https://doi.org/10.1109/TNB.2017.2771350
  21. J. Jeng, S. Hsu, Y. Chang, "Entropy improvement for fractal image coder," Int Arab J Inf Technol, vol. 9,no. 5, pp. 403-410, Sep. 2012.
  22. M. D. Albuquerque, I. Esquef, A. Mello, Mello, "Image thresholding using Tsallis entropy," Pattern Recognition Letters, vol. 25, no.9, pp. 1059-1065, July, 2004.. https://doi.org/10.1016/j.patrec.2004.03.003
  23. R. Adams, L. Bischof, "Seeded region growing," IEEE Transactions on pattern analysis and machine intelligence, vol. 16, no. 6, pp. 641-647, Jun. 1994. https://doi.org/10.1109/34.295913