Region covariance descriptors calculated over the salient points for target tracking
Author
Çakir, S.
Aytaç, T.
Yildirim, A.
Beheshti, S.
Gerek Ö.N.
Çetin, A. Enis
Date
2012Source Title
2012 20th Signal Processing and Communications Applications Conference (SIU)
Publisher
IEEE
Language
Turkish
Type
Conference PaperItem Usage Stats
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Abstract
Features extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasi's minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure. © 2012 IEEE.
Keywords
Classical approachClassical methods
Computational costs
Descriptors
Eigenvalue methods
Image statistics
Pixel location
Region covariance
Region covariance descriptors
Salient points
Tracking accuracy
Eigenvalues and eigenfunctions
Signal processing
Target tracking