Is There Anybody Out There?

  • Thomas B. MoeslundEmail author


Applications within the field of Looking at People only make sense when the analyzed imagery contains one or more humans. The first step in such systems is therefore to determine if one or more humans are present and where in the scene they are. Moreover, since many applications require a number of consecutive frames containing people in order to do any processing, tracking of individuals is often a requirement. This part provides an overview of detection and tracking methods though six different chapters ranging from body and face detection, over tracking of multiple people to an overview of public benchmarking datasets for evaluation. This first chapter in this part introduces the remaining chapters and finally provides pointers to possible future research directions within these fields.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, Boston (1988) zbMATHGoogle Scholar
  6. 6.
    Bradski, G., Kaehler, A.: Learning Opencv. O’Reilly Media Inc., Sebastopol (2008). Google Scholar
  7. 7.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Vision and Pattern Recognition (2005) Google Scholar
  8. 8.
    Doshi, A., Trivedi, M.M.: Satellite imagery based robust, adaptive background models and shadow suppression. J. VLSJ Signal Process. 1(2), 119–132 (2007) zbMATHGoogle Scholar
  9. 9.
    Ferrari, V., Marin, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (2008) Google Scholar
  10. 10.
    Fihl, P., Corlin, R., Park, S., Moeslund, T.B., Trivedi, M.M.: Tracking of individuals in very long video sequences. In: International Symposium on Visual Computing, Lake Tahoe, Nevada, USA (2006) Google Scholar
  11. 11.
    Grimson, W.E.L., Stauffer, C.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (1999) Google Scholar
  12. 12.
    Huerta, I., Holte, M., Moeslund, T.B., Gonzàlez, J.: Detection and removal of chromatic moving shadows in surveillance scenarios. In: International Conference on Computer Vision, Kyoto, Japan (2009) Google Scholar
  13. 13.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing (2004) Google Scholar
  14. 14.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104, 90–126 (2006) CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM SIGGRAPH (2004) Google Scholar
  17. 17.
    Stalder, S., Grabner, H., Van Gool, L.: Cascaded confidence filter for improved tracking-by-detection. In: European Conference on Computer Vision (2010) Google Scholar
  18. 18.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Vision and Pattern Recognition, pp. 511–518 (2001) Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Architecture, Design and Media TechnologyAalborg UniversityAalborgDenmark

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