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Adaptive Moving Shadows Detection Using Local Neighboring Information

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

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Abstract

In this paper, we propose an adaptive approach to the detection of moving shadows by employing local neighboring information. The process of the proposed approach is mainly operated by three steps: the first step is to detect the candidate shadows by RGB ratio; the second step is to extract partial accurate shadows in order to estimate accurate threshold parameters of shadow detectors; the final step is to utilize three detectors to detect real shadows from candidate shadows. The main contributions of this paper include two parts: an effective method of candidate shadows detection is presented; an adaptive mechanism of estimating threshold parameters is designed. Moreover, three detectors that consist of color, texture and gradient features are jointly utilized to detect shadows at pixel-level. Experimental results on a benchmark suit of indoor and outdoor video sequences demonstrated the proposed approach’s effectiveness.

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References

  1. Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 918–923 (2003)

    Article  Google Scholar 

  2. St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)

    Article  MathSciNet  Google Scholar 

  3. Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1337–1342 (2003)

    Article  Google Scholar 

  4. Hsieh, J.W., Hu, W.F., Chang, C.J., Chen, Y.S.: Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image Vis. Comput. 21(6), 505–516 (2003)

    Article  Google Scholar 

  5. Huang, J.B., Chen, C.S.: Moving cast shadow detection using physics-based features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2310–2317 (2009)

    Google Scholar 

  6. Leone, A., Distante, C.: Shadow detection for moving objects based on texture analysis. Pattern Recogn. 40(4), 1222–1233 (2007)

    Article  MATH  Google Scholar 

  7. Sanin, A., Sanderson, C., Lovell, B.C.: Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recogn. 45(4), 1684–1695 (2012)

    Article  Google Scholar 

  8. Wang, B., Zhu, W., Zhao, Y., Zhang, Y.: Moving cast shadow detection using joint color and texture features with neighboring information. In: Huang, F., Sugimoto, A. (eds.) PSIVT 2015. LNCS, vol. 9555, pp. 15–25. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30285-0_2

    Chapter  Google Scholar 

  9. Zhang, W., Fang, X.Z., Yang, X.K., Wu, Q.M.J.: Moving cast shadows detection using ratio edge. IEEE Trans. Multimed. 9(6), 1202–1214 (2007)

    Article  Google Scholar 

  10. Qin, R., Liao, S., Lei, Z., Li, S.Z.: Moving cast shadow removal based on local descriptors. In: 2010 International Conference on Pattern Recognition, pp. 1377–1380 (2010)

    Google Scholar 

  11. Russell, M., Zou, J.J., Fang, G.: Real-time vehicle shadow detection. Electron. Lett. 51(16), 1253–1255 (2015)

    Article  Google Scholar 

  12. Choi, J.M., Chang, H.J., Yoo, Y.J., Jin, Y.C.: Robust moving object detection against fast illumination change. Comput. Vis. Image Underst. 116(2), 179–193 (2012)

    Article  Google Scholar 

  13. Jiang, K., Li, A.H., Cui, Z.G., Wang, T., Su, Y.Z.: Adaptive shadow detection using global texture and sampling deduction. J. IET Comput. Vis. 7(2), 115–122 (2013)

    Article  Google Scholar 

  14. Wang, J., Wang, Y., Jiang, M., Yan, X., Song, M.: Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis. J. Vis. Commun. Image Represent. 25(5), 978–993 (2014)

    Article  Google Scholar 

  15. Dai, J., Han, D., Zhao, X.: Effective moving shadow detection using statistical discriminant model. Optik - Int. J. Light Electron Opt. 126(24), 5398–5406 (2015)

    Article  Google Scholar 

  16. Huerta, I., Holte, M.B., Moeslund, T.B., Gonzàlez, J.: Chromatic shadow detection and tracking for moving foreground segmentation. Image Vis. Comput. 41(C), 42–53 (2015)

    Article  Google Scholar 

  17. Kar, A., Deb, K.: Moving cast shadow detection and removal from video based on HSV color space. In: International Conference on Electrical Engineering and Information Communication Technology (2015)

    Google Scholar 

  18. Al-Najdawi, N., Bez, H.E., Singhai, J., Edirisinghe, E.A.: A survey of cast shadow detection algorithms. Pattern Recogn. Lett. 33(6), 752–764 (2012)

    Article  Google Scholar 

  19. The Test Sequences are from: https://sourceforge.net/projects/arma/files/

  20. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic and Computer Engineering, Shenzhen Graduate School of Peking University, Shenzhen, China

    Bingshu Wang, Yule Yuan & Yong Zhao

  2. College of Information Engineering, Shenzhen University, Shenzhen, China

    Wenbin Zou

Authors
  1. Bingshu Wang
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  2. Yule Yuan
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  3. Yong Zhao
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  4. Wenbin Zou
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Corresponding author

Correspondence to Yong Zhao .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Wang, B., Yuan, Y., Zhao, Y., Zou, W. (2017). Adaptive Moving Shadows Detection Using Local Neighboring Information. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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