Moving Cast Shadows Detection Methods for Video Surveillance Applications

  • Ariel Amato
  • Ivan Huerta
  • Mikhail G. Mozerov
  • F. Xavier Roca
  • Jordi Gonzàlez
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 6)


Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).


Local Binary Pattern Video Surveillance Foreground Pixel Normalize Cross Correlation Cast Shadow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Consolider-Ingenio 2010: MIPRCV (CSD200700018); Avanza I+D ViCoMo (TSI-020400-2009-133) and DiCoMa (TSI-020400-2011-55); along with the Spanish projects TIN2009-14501-C02-01 and TIN2009-14501-C02-02.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ariel Amato
    • 1
  • Ivan Huerta
    • 2
  • Mikhail G. Mozerov
    • 1
  • F. Xavier Roca
    • 1
  • Jordi Gonzàlez
    • 1
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
  2. 2.Institut de Robòtica i Informàtica industrialUniversitat Politècnica de CatalunyaBarcelonaSpain

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