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Automatic Removal of Complex Shadows From Indoor Videos

Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc804942
Date08 1900
CreatorsMohapatra, Deepankar
ContributorsYuan, Xiaohui, Fu, Song, Swigger, Kathleen M.
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatviii, 53 pages : illustrations (some color), Text
RightsPublic, Mohapatra, Deepankar, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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