A Multi-layer Scene Model for Video Surveillance Applications

  • Chung-Hsien Huang
  • Ruei-Cheng Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


Foreground detection is the most important preprocess for video surveillance applications. However, classifying pixels of video frames into only background and foreground seems insufficient in real situations. In this study, we model the monitoring scene by using a multi-layer framework. The proposed scene model classifies pixels layer by layer into four different states, comprising background, moving foreground, static foreground and shadow. Different scenarios such as shadow elimination, abandoned object detection and object tracking were tested with the proposed scene model. The experimental results demonstrate it is quantified for real video surveillance applications.


video surveillance background subtraction shadow elimination 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Sirotti, S.: Improving Shadow Suppression in Moving Object Detection with HSV Color Information. In: Proceedings of 2001 IEEE Intelligent Transportation Systems Conference, pp. 334–339 (2001)Google Scholar
  2. 2.
    Shan, Y., Yang, F., Wang, R.: Color Space Selection for Moving Shadow Elimination. In: Proceedings of 4th International Conference on Image and Graphics, pp. 496–501 (2007)Google Scholar
  3. 3.
    Nicolas, M.-B., Zaccarin, A.: Learning and Removing Cast Shadows through a Multidistribution Approach. IEEE Transactions on Pattern Analysis and Machine Intelligent 29(7), 1133–1146 (2007)CrossRefGoogle Scholar
  4. 4.
    Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-time Tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)Google Scholar
  5. 5.
    Tanaka, T., Shimada, A., Arita, D., Taniguchi, R.: Non-parametric Background and Shadow Modeling for Object Detection. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 159–168. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Herrero-Jaraba, E., Orrite-Urunuela, C., Senar, J.: Detected Motion Classification with a Double-background and a neighborhood-based difference. Pattern Recognition Letters 24, 2079–2092 (2003)CrossRefGoogle Scholar
  7. 7.
    Gallego, J., Pardas, M., Landabaso, J.-L.: Segmentation and Tracking of Static and Moving Objects in Video Surveillance Scenarios. In: Proceedings of IEEE International Conference on Image Processing, pp. 2716–2719 (2008)Google Scholar
  8. 8.
    Benedek, C., Sziranyi, T.: Study on Color Space Selection for Detecting Cast Shadows in Video Surveillance. International Journal of Imaging Systems and Technology 17, 190–201 (2007)CrossRefGoogle Scholar
  9. 9.
    Izadi, M., Parvaneh, S.: Robust Region-based Background Subtraction and Shadow Removing using Color and Gradient Information. In: Proceedings of International Conference on Pattern Recognition, pp. 1–5 (2008)Google Scholar
  10. 10.
    Zivkovic, Z., van der Heijden, F.: Recursive Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligent 26(7), 773–780 (2006)Google Scholar
  11. 11.
    Marcenaro, L., Ferrari, M., Marchesotti, L., Regazzoni, C.S.: Multiple Object Tracking Under Heavy Occlusions by Using Kalman Filters Based on Shape Matching. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 341–344 (2002)Google Scholar
  12. 12.
    Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting Moving Shadows: Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligent 25(7), 918–923 (2003)CrossRefGoogle Scholar
  13. 13.
    Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2006) (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chung-Hsien Huang
    • 1
  • Ruei-Cheng Wu
    • 1
  1. 1.Information and Communications Research LaboratoriesIndustrial Technology Research InstituteTaiwan

Personalised recommendations