Advertisement

Scale-Relation Feature for Moving Cast Shadow Detection

  • Chih-Wei LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

Abstract

Cast shadow is the problem of moving cast detection in visual surveillance applications, which has been studied over years. However, finding an efficient model that can handle the issue of moving cast shadow in various situations is still challenging. Unlike prior methods, we use a data-driven method without the strong parametric assumptions or complex models to address the problem of moving cast shadow. In this paper, we propose a novel feature-extracting framework called Scale-Relation Feature Extracting (SRFE). By leveraging the scale space, SRFE decomposes each image with various properties into various scales and further considers the relationship between adjacent scales of the two shadow properties to extract the scale-relation features. To seek the criteria for discriminating moving cast shadow, we use random forest algorithm as the ensemble decision scheme. Experimental results show that the proposed method can achieve the performances of the popular methods on the widely used dataset.

Keywords

Shadow removal Scale-relation Ensemble decision scheme Random forest 

Notes

Acknowledgements

This work was supported in part by the Natural Science Foundation of Fujian Province of China, under grant 2016J01718.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Huang, J.B., Chen, C.S.: Moving cast shadow detection using physics-based features. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2310–2317. IEEE (2009)Google Scholar
  6. 6.
    Koenderink, J.J.: The structure of images. Biol. Cybern. 50(5), 363–370 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Leone, A., Distante, C.: Shadow detection for moving objects based on texture analysis. Pattern Recogn. 40(4), 1222–1233 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(1–2), 225–270 (1994)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Sanin, A., Sanderson, C., Lovell, B.C.: Improved shadow removal for robust person tracking in surveillance scenarios. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 141–144. IEEE (2010)Google Scholar
  12. 12.
    Sanin, A., Sanderson, C., Lovell, B.C.: Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recogn. 45(4), 1684–1695 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhouChina

Personalised recommendations