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Recent Research Trends and Methods Used in Moving Object Detection

  • Tannistha PalEmail author
  • Arindam Deb
  • Anwesha Roy
  • Praseed Debbarma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 989)

Abstract

Object detection is one of the main challenges in the field of image processing from the beginning. Many methods had been established and proposed through the years, but none of them could give a high accuracy rate along with low computation time. Background subtraction is a key technique in object detection in security and surveillance purpose with a high accuracy rate, but it comes in a cost of high computational time. Frame differencing came with low computational time, but it cannot give us the accuracy that we want. Optical flow and temporal differencing are independent of irregularities of light features, but accuracy is not up to the mark. The main obstacles of detecting an object in a video are still present in proposed techniques till date. In this paper, we mainly describe the recent trends and works done in the field of object detection. We have discussed various important methods of object detection and pointed out their positive aspects and limitations.

Keywords

Object detection Video surveillance Object classification 

References

  1. 1.
    Barnich, O., Van Droogenbroeck, M.: Vibe: a universal background sub-traction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Alawi, M.A., Khalifa, O.O., Islam, M.R.: Performance comparison of background estimation algorithms for detecting moving vehicle. World Appl. Sci. J. 21(Mathematical Applications in Engineering), 109–114 (2013). ISSN 1818–4952, IDOSI Publications, 2013,  https://doi.org/10.5829/idosi.wasj.2013.21.mae.99934
  3. 3.
    Nascimento, J.C., Marques, J.S.: Performance evaluation of object detection algorithms for video surveillance. IEEE Trans. Multimed. 8(4) (2006)CrossRefGoogle Scholar
  4. 4.
    Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 16–21, June 2012Google Scholar
  5. 5.
    Zhan, C., Duan, X., Xu, S., Song, Z., & Luo, M.: An improved moving object detection algorithm based on frame difference and edge detection. IEEE (2007)Google Scholar
  6. 6.
    Elhabian, S.Y., El-Sayed, K.M., Ahmed, S.H.: Moving object detection in spatial domain using background removal techniques—State-of-art. Recent. Pat.S Comput. Sci. 1, 32–54 (2008)Google Scholar
  7. 7.
    Del-Blanco, C.R., Jaureguizar, F., García, N.: An efficient multiple object detection and tracking framework for automatic counting and video surveillance applications. IEEE Trans. Consum. Electron. 58(3), 857–862 (2012)CrossRefGoogle Scholar
  8. 8.
    Kartika, I., Mohamed, S.S.: Frame differencing with post-processing techniques for moving object detection in outdoor environment. IEEE (2011)Google Scholar
  9. 9.
    Srivastav, N., Agrwal, S.L., Gupta, S.K., Srivastava, S.R., Chacko, B., Sharma, H.: Hybrid object detection using improved three frame differencing and background subtraction. In: 7th International Conference on Cloud Computing, Data Science & Engineering—Con-fluence (2017)Google Scholar
  10. 10.
    Murali, S., Girisha, R.: Segmentation of motion objects from surveillance video sequences using temporal differencing combined with multiple correlation. Adv. Video Signal Based Surveill. J. (2009)Google Scholar
  11. 11.
    Liang, K., Wang, J., Zhao, T.: Variation temporal differencing for moving target detecting and tracking. In: International Conference on Electronics, Communications and Control (ICECC) (2011)Google Scholar
  12. 12.
    Tiwari, V., Chaudhary, D., Tiwari, V.: Foreground segmentation using GMM combined temporal differencing. In: International Conference on Computer, Communications, and Electronics (Comptelix). Malaviya National Institute of Technology Jaipur, 01–02 July 2017Google Scholar
  13. 13.
    Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: Proceedings of IEEE Work-shop on Change Detection (2012)Google Scholar
  14. 14.
    Xiang, J., Fan, H., Liao, H., Xu, J., Sun, W., Yu, S.: Moving object detection and shadow removing under chang-ing illumination condition, pp. 1–10. Hindawi Publishing Corporation, Mathematical Problems in Engineering, Feb 2014Google Scholar
  15. 15.
    Hao, J., Li, C., Kim, Z., Xiong, Z.: Spatiotemporal traffic scene modeling for object motion detection. IEEE, Intell. Trans. Syst. (2012)Google Scholar
  16. 16.
    Maddalena, L., Petrosino, A.: The 3dSOBS + algorithm for moving object detection. Comput. Vis. Image Underst. 65–73 (2014)CrossRefGoogle Scholar
  17. 17.
    Zivkovic, Z.: Improved adaptive gaussian mixture model for back-ground subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2 (2004)Google Scholar
  18. 18.
    Manzanera, A., Richefeu, J.C.: A new motion detection algorithm based on Σ-background estimation. Pattern Recognit. Lett. 28, 320–328 (2007)CrossRefGoogle Scholar
  19. 19.
    Choi, J.M., Chang, H.J., Yoo, Y.J., Choi, J.Y.: Robust Moving Object Detection Against Fast Illumination Change. Elsevier (2011)Google Scholar
  20. 20.
    Wang, Z., Liao, K., Xiong, J., Zhang, Q.: Moving object detection based on temporal information. IEEE Signal Process. Lett. 21(11), 1404–1407 (2014)CrossRefGoogle Scholar
  21. 21.
    Lin, L., Liu, B., Xiao, Y.: An object tracking method based on CNN and optical flow. In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Tannistha Pal
    • 1
    Email author
  • Arindam Deb
    • 1
  • Anwesha Roy
    • 1
  • Praseed Debbarma
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyAgartala, Barjala, JiraniaIndia

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