A fast valley-based segmentation for detection of slowly moving objects

Original Paper
  • 30 Downloads

Abstract

Moving object detection in a video sequence is the first and most important step in many computer vision applications. However, it is challenging for a machine to match with the human visual perception level. Motion information of slowly moving object is highly erroneous in comparison with fast moving object. Therefore, in real time, accurate segmentation of slowly moving objects is more challenging. In this paper, a fast and efficient segmentation algorithm is proposed for the detection of slowly moving object in a video sequence. The proposed method has three steps to extract the slowly moving object in a video. In the first step, an averaging frame difference method is proposed to extract the motion information. In the second step, a valley-based thresholding is proposed to segment all the frames of a video. In the final step, the motion information and spatial homogeneous region information are merged to extract the slowly moving object.

Keywords

Spatial segmentation Temporal segmentation Thresholding Slowly moving object detection Video surveillance 

References

  1. 1.
    Moeslund, T.B.: Introduction to Video and Image Processing. Springer, London (2012)CrossRefMATHGoogle Scholar
  2. 2.
    Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Fourth IEEE Workshop on Application of Computer Vision, pp. 8–14 (1998)Google Scholar
  3. 3.
    Karavasilis, V., Nikou, C., Likas, A.: Visual tracking using spatially weighted likelihood. Comput. Vis. Image Underst. 140, 43–57 (2015)CrossRefGoogle Scholar
  4. 4.
    Pragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Interface 22(3), 266–280 (2000)Google Scholar
  5. 5.
    Duncan, J.H., Chou, T.-C.: On the detection of motion and the computation of optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 14(3), 346–352 (1992)CrossRefGoogle Scholar
  6. 6.
    Choudhury, J.H., Sa, P.K., Bakshi, S., Majhi, B.: An evaluation of background subtraction for object detection vis-a-vis mitigating challenging scenarios. IEEE Access 4, 6133–6150 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhu, Z., Wang, Y.: A hybrid algorithm for automatic segmentation of slowly moving objects. AEU Int. J. Electron. Commun. 66, 249–254 (2012)CrossRefGoogle Scholar
  8. 8.
    Neri, A., Colonnese, S., Russo, G., Talone, P.: Automatic moving object and background separation. Signal Process. 66, 219–232 (1998)CrossRefMATHGoogle Scholar
  9. 9.
    Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11–12, 31–66 (2014)CrossRefMATHGoogle Scholar
  10. 10.
    Wang, Z., Liao, K., Xiong, J., Zhang, Q.: Moving object detection based on temporal information. IEEE Signal Process. Lett. 21(11), 1403–1407 (2014)CrossRefGoogle Scholar
  11. 11.
    Zhang, R., Liu, X., Hu, J., Chang, K., Liu, K.: A fast method for moving object detection in video surveillance image. SIViP 11, 841 (2017).  https://doi.org/10.1007/s11760-016-1030-2 CrossRefGoogle Scholar
  12. 12.
    Zheng, X.S., Zhao Y.L., Li, N., Wu, H.M.: An automatic moving object detection algorithm for video surveillance applications. In: Proceedings of the International Conference on Embedded Software and System, pp. 542–543 (2009)Google Scholar
  13. 13.
    Sengar, S.S., Mukhopadhyay, S.: Moving object area detection using normalized self adaptive optical flow. Opt. Int. J. Light Electron Opt. 127(16), 6258–6267 (2016)CrossRefGoogle Scholar
  14. 14.
    Sengar, S.S., Mukhopadhyay, S.: Moving object detection based on frame difference and W4. Signal Image Video Process. (2017).  https://doi.org/10.1007/s11760-017-1093-8
  15. 15.
    Wu, H., Liu, X., Luo, X., Su, J.: Real-time background subtraction-based video surveillance of people by integrating local texture patterns. SIViP 8, 665–676 (2014)CrossRefGoogle Scholar
  16. 16.
    Dou, J., Qin, Q., Tu, Z.: Background subtraction based on circulant matrix. SIViP 11, 407–414 (2017)CrossRefGoogle Scholar
  17. 17.
    Xia, H., Song, S., He, L.: A modified Gaussian mixture background model via spatiotemporal distribution with shadow detection. SIViP 10, 343–350 (2016)CrossRefGoogle Scholar
  18. 18.
    Derf’s video collection is available on website. https://media.xiph.org/video/derf/
  19. 19.
    YUV video sequences are available on trace. http://trace.eas.asu.edu/yuv
  20. 20.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. PAMI 23(8), 800–810 (2001)CrossRefGoogle Scholar
  22. 22.
    Singla, A., Patra, S.: A fast automatic optimal threshold selection technique for image segmentation. SIViP 11(2), 243–250 (2017)CrossRefGoogle Scholar
  23. 23.
    Duan, J., Qiu, G.: Novel histogram processing for colour image enhancement. In: Proceedings of Third International Conference on Image and Graphics (ICIG’04) (2005)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centurion University of Technology and Management (CUTM)ParalakhemundiIndia
  2. 2.C. V. Raman College of EngineeringBhubaneswarIndia

Personalised recommendations