The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.07 - July (2007 vol.29)
pp: 1133-1146
ABSTRACT
Moving cast shadows are a major concern for foreground detection algorithms. The processing of foreground images in surveillance applications typically requires that such shadows be identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of nonuniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, including light saturated areas, and prevent false detection in regions where shadows cannot be detected. The proposed approach can be used with pixel-based descriptions of shadowed surfaces found in the literature. It significantly reduces their false detection rate without increasing the missed detection rate. Results obtained with different scene types and shadow models show the robustness of the approach.
INDEX TERMS
Shadow detection, GMM, GMSM, background subtraction, multidistribution, segmentation, image models, pixel classification.
CITATION
Nicolas Martel-Brisson, Andr? Zaccarin, "Learning and Removing Cast Shadows through a Multidistribution Approach", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.29, no. 7, pp. 1133-1146, July 2007, doi:10.1109/TPAMI.2007.1039
16 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool