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Improved kernel-based object tracking under occluded scenarios

DSpace at IIT Bombay

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Title Improved kernel-based object tracking under occluded scenarios
 
Creator NAMBOODIRI, VP
GHORAWAT, A
CHAUDHURI, S
 
Description A successful approach for object tracking has been kernel based object tracking [1] by Comaniciu et al.. The method provides an effective solution to the problems of representation and localization in tracking. The method involves representation of an object by a feature histogram with an isotropic kernel and performing a gradient based mean shift optimization for localizing the kernel. Though robust, this technique fails under cases of occlusion. We improve the kernel based object tracking by performing the localization using a generalized (bidirectional) mean shift based optimization. This makes the method resilient to occlusions. Another aspect related to the localization step is handling of scale changes by varying the bandwidth of the kernel. Here, we suggest a technique based on SIFT features [2] by Lowe to enable change of bandwidth of the kernel even in the presence of occlusion. We demonstrate the effectiveness of the techniques proposed through extensive experimentation on a number of challenging data sets.
 
Publisher SPRINGER-VERLAG BERLIN
 
Date 2011-10-23T21:12:10Z
2011-12-15T09:11:03Z
2011-10-23T21:12:10Z
2011-12-15T09:11:03Z
2006
 
Type Proceedings Paper
 
Identifier Computer Vision, Graphics and Image Processing, Proceedings,4338,504-515
978-3-540-68301-8
0302-9743
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15237
http://hdl.handle.net/100/1820
 
Source 5th Indian Conference on Computer Vision, Graphics and Image Processing,Madurai, INDIA,DEC 13-16, 2006
 
Language English