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Proceedings Paper

Performance analysis of a modified moving shadow elimination method developed for indoor scene activity tracking
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Paper Abstract

Moving shadow detection is an important step in automated robust surveillance systems in which a dynamic object is to be segmented and tracked. Rejection of the shadow region significantly reduces the erroneous tracking of non-target objects within the scene. A method to eliminate such shadows in indoor video sequences has been developed by the authors. The objective has been met through the use of a pixel-wise shadow search process that utilizes a computational model in the RGB colour space to demarcate the moving shadow regions from the background scene and the foreground objects. However, it has been observed that the robustness and efficiency of the method can be significantly enhanced through the deployment of a binary-mask based shadow search process. This, in turn, calls for the use of a prior foreground object segmentation technique. The authors have also automated a standard foreground object segmentation technique through the deployment of some popular statistical outlier-detection based strategies. The paper analyses the performance i.e. the effectiveness as a shadow detector, discrimination potential, and the processing time of the modified moving shadow elimination method on the basis of some standard evaluation metrics.

Paper Details

Date Published: 3 October 2008
PDF: 10 pages
Proc. SPIE 7119, Optics and Photonics for Counterterrorism and Crime Fighting IV, 71190A (3 October 2008); doi: 10.1117/12.800376
Show Author Affiliations
Bhargav Kumar Mitra, Univ. of Sussex (United Kingdom)
Muhammad Kamran Fiaz, Univ. of Sussex (United Kingdom)
Ioannis Kypraios, Univ. of Sussex (United Kingdom)
Philip Birch, Univ. of Sussex (United Kingdom)
Rupert Young, Univ. of Sussex (United Kingdom)
Chris Chatwin, Univ. of Sussex (United Kingdom)


Published in SPIE Proceedings Vol. 7119:
Optics and Photonics for Counterterrorism and Crime Fighting IV
Gari Owen, Editor(s)

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