NDLI: Detection of moving shadows using mean shift clustering and a significance test

Content Provider	IEEE Xplore Digital Library
Author	Toth, D. Stuke, I. Wagner, A. Aach, T.
Copyright Year	2004
Description	Author affiliation: Inst. for Signal Process., Luebeck Univ., Germany (Toth, D.; Stuke, I.; Wagner, A.; Aach, T.)
Abstract	An algorithm that discriminates moving objects from their shadows is presented. Starting from the change mask of an image sequence, first of all the changed area is divided into subregions consisting of pixels with similar colour properties. This is done using the mean shift algorithm, which is very powerful in non-parametric clustering of data. In a second step a significance test is performed to classify each image pixel inside the change mask into one of the classes foreground or shadow. To do this a straightforward image model is used where the grey level of a foreground pixel covered by a shadow is given by the product of the corresponding background pixels' grey-level and a constant value. Assuming that fore- and background images are corrupted by Gaussian white noise, a significance test is derived which classifies all pixels inside the change mask. In the third step global and local information from the first and second steps are combined. For each region inside the change mask it is examined if the majority of pixels survived the second step. If this is the case, the whole region is kept for the final moving object mask, if not the region is set to zero.
Starting Page	260
Ending Page	263
File Size	415456
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1333753
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Pixel Signal processing algorithms Change detection algorithms Clustering algorithms Performance evaluation Target recognition Traffic control Image sequences White noise
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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