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A new method of moving object detection using adaptive filter

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Abstract

In many real-world video analysis systems , the available resources are constrained, which limits the image resolution. However, the low computational complexity and fast response for low-resolution images still make them attractive for computer vision applications. This work presents a new model that uses a least-mean-square scheme to train the mask operation for low-resolution images. This efficient and real-time method, which uses an adaptive least-mean-square scheme (ALMSS), uses the training mask to detect moving objects on resource-limited systems. The detection of moving objects is a basic and important task in video surveillance systems, which affects the results of any post-processing, such as object classification, object identification and the description of object behaviors. However, the detection of moving objects in a real environment is a difficult task because of noise issues, such as fake motion or noise. The ALMSS method effectively reduces computational cost for both fake motion environment. The experiments using real scenes indicate that the proposed ALMSS method is effective in the real-time detection of moving objects. This method can be implemented in hardware for high-resolution applications, such as full-HD images. A prototype VLSI circuit is designed and simulated using a TSMC 0.18 μm 1P6M process.

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References

  1. Hu, W.-M., Tan, T.-N., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. In: IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, vol. 34, no. 3, pp. 334–352, Aug 2004

  2. Jacobs, N., Pless, R.: Time scales in video surveillance. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1106–1113 (2008)

    Article  Google Scholar 

  3. Cheng, F.-H., Chen, Y.-L.: Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recogn. 39(3), 1126–1139 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Huang, K.-Q., Wang, L.-S., Tan, T.-I., Maybank, S.: A real-time objects detecting and tracking system for outdoor night surveillance. Pattern Recogn. 41(1), 423–444 (2008)

    Article  MATH  Google Scholar 

  5. Gonzalez, R.C., Woods, R.E.: Digital image processing. Addison-Wesley Longman, Boston (2001)

    Google Scholar 

  6. Collins, R.T., Lipton, A.J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.L.: A system for video surveillance and monitoring. Carnegie Mellon University, Technical Report, CMU-RI-TR-00-12 (2000)

  7. Lhuillier, M., Quan, L.: Image-based rendering by joint view triangulation. IEEE Trans. Circuits Syst. Video Technol. 13(11), 1051–1063 (2003)

    Article  Google Scholar 

  8. Bennett, E.P., McMillan, L.: Video enhancement using per-pixel virtual exposures. ACM 24(3), 845–852 (2005)

    Google Scholar 

  9. Alsaqre, F.E., Baozong, Y.: Multiple moving objects tracking for video surveillance system. In: IEEE International Conference on Signal Processing, vol. 2, pp. 1301–1305, Aug 2004

  10. Sugandi, B., Kim, H., Tan, J.K., Ishikawa, S.: Real time tracking and identification of moving persons by using a camera in outdoor environment. Int. J. Innov. Comput. Inf. Control 5(5), 1179–1188 (2009)

    Google Scholar 

  11. Haykin, S.: Adaptive Filter Theory, 2nd Edn. Prentice Hall, (1991)

  12. Cvetkovic, S., Bakker, P., Schirris, J.: Background estimation and adaptation model with light-change removal for heavily down-sampled video surveillance signals. In: IEEE International Conference on Image Processing, pp. 1829–1832, Oct 2006

  13. Huang, J.-C., Su, T.-S., Wang, L.-J., Hsieh, W.-S.: Double-change-detection method for wavelet-based moving object segmentation. Electron. Lett. 40(13), 798–799 (2004)

    Article  Google Scholar 

  14. Yamaoka, K., Morimoto, T., Adachi, H., Awane, K., Koide, T., Mattausch, H.J.: Multi-object tracking VLSI architecture using image-scan based region growing and feature matching. In: IEEE International Conference on Circuits and Systems, vol. 19, no 8, pp. 5575–5578, May 2006

  15. Hsieh, C.-C., Hsu, S.-S.: A simple and fast surveillance system for human tracking and behavior analysis. In: IEEE Conference on Signal-Image Technologies and Internet-Based System, pp. 812–828, Dec 2007

  16. Cheng, C.-C., Lin, C.-H., Li, C.-T., Chen, L.-G.: iVisual: an intelligent visual sensor SoC with 2790 fps CMOS image sensor and 205GOPS/W vision processor. IEEE J. Solid State Circuits 44(1), 127–135 (2009)

    Article  Google Scholar 

  17. Hsia, C.-H., Guo, J.-M., Chiang, J.-S.: Improved low-complexity algorithm for 2-D integer lifting-based discrete wavelet transform using symmetric mask-based scheme. IEEE Trans. Circuits Syst. Video Technol. 19(8), 1201–1208 (2009)

    Google Scholar 

  18. Yang, S.-W., Sheu, M.-H., Lin, J.-J., Hu, C.-C., Chen, T.-H., Tseng, S.-Y.: Parallel 3-pixel labeling method and its hardware architecture design. In: IEEE International Conference on Information Assurance and Security, vol. 1, pp. 185–188, Aug 2009

  19. Huang, J.-C., Hsieh, W.-S.: Wavelet-based moving object segmentation. Electron. Lett. 39(39), 1380–1382 (2003)

    Article  Google Scholar 

  20. Albusac, J., Vallejo, D., Castro-Schez, J.J., Jimenez-Linares, L.: OCULUS surveillance system: fuzzy on-line speed analysis from 2D images. Expert Syst. Appl. 38(10), 12791–12806 (2011)

    Article  Google Scholar 

  21. Hsia, C.-H., Guo, J.-M.: Improved directional lifting-based discrete wavelet transform for low resolution moving object detection. In: IEEE International Conference on Image Processing, pp. 2457–2460, Sep 2012

  22. Performance evaluation of surveillance systems [Online]. Available: http://www.research.ibm.com/peoplevision/performanceevaluation.html

  23. Hsia, C.-H., Yeh, Y.-P., Wu, T.-C., Chiang, J.-S., Liou, Y.-J.: Low resolution method using adaptive LMS scheme for moving objects detection and tracking. In: IEEE International Symposium on Intelligent Signal Processing and Communications Systems, pp. 129–132, Dec 2010

  24. A change detection benchmark dataset (from baseline pattern: “Pedestrains”) [Online]. Available: http://www.changedetection.net

  25. Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8, June 2012

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Acknowledgments

This research work was partially supported by the National Science Council of Taiwan, R.O.C., under Grant number NSC-99-2221-E-032-028.

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Authors and Affiliations

  1. Department of Electrical Engineering, Chinese Culture University, Taipei City, Taiwan

    Chih-Hsien Hsia

  2. Department of Electrical Engineering, Tamkang University, New Taipei City, Taiwan

    Tsung-Cheng Wu & Jen-Shiun Chiang

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  1. Chih-Hsien Hsia
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  2. Tsung-Cheng Wu
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  3. Jen-Shiun Chiang
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Correspondence to Jen-Shiun Chiang.

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Hsia, CH., Wu, TC. & Chiang, JS. A new method of moving object detection using adaptive filter. J Real-Time Image Proc 13, 311–325 (2017). https://doi.org/10.1007/s11554-014-0404-3

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