Silhouette extraction using color and depth information

Ekaterina Tolstaya; Victor Bucha

doi:10.1117/12.907690

30 January 2012 Silhouette extraction using color and depth information

Ekaterina Tolstaya, Victor Bucha

Proceedings Volume 8290, Three-Dimensional Image Processing (3DIP) and Applications II; 82900B (2012) https://doi.org/10.1117/12.907690
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

Recently applications involving capture of scenes with object of interest among surroundings gained high popularity. Such applications include video surveillance, human motion capture, human-computer interaction, etc. For proper analysis of the object of interest a necessary step is to separate the object of interest from surroundings, i. e. perform background subtraction (or silhouette extraction). This is a challenging task because of several problems, which are slight changes in background; shadows caused by the object of interest; and similarly colored objects. In this work we propose a new method for extracting the silhouette of an object of interest, based upon the joint use of both depth (range) and color data. Depth-based data is independent of color image data, and hence not affected by the limitations associated with color-based segmentation, such as shadows and similarly colored objects. At the initial moment an image of the background (not containing the object of interest) is present, and it is updated in every frame taking into account extracted silhouette and using "running average". Silhouette extraction method is based on k-means clustering of depth data and color difference data, and per-pixel silhouette mask computation, using clusters' centroids. The proposed solution is very fast and allows real-time processing of video. Developed algorithm has been successfully applied in human recognition application and provided good results for modeling human figure.

Citation Download Citation

Ekaterina Tolstaya and Victor Bucha "Silhouette extraction using color and depth information", Proc. SPIE 8290, Three-Dimensional Image Processing (3DIP) and Applications II, 82900B (30 January 2012); https://doi.org/10.1117/12.907690

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KEYWORDS

Image segmentation

Digital filtering

Image processing

Video

Video surveillance

Nonlinear filtering

Algorithm development

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