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Optical Engineering

Salient point region covariance descriptor for target tracking
Author(s): Serdar Cakir; Tayfun Aytaç; Alper Yildirim; Soosan Beheshti; Oemer N. Gerek; Ahmet E. Cetin
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Paper Abstract

Features extracted at salient points are used to construct a region covariance descriptor (RCD) for target tracking. In the classical approach, the RCD is computed by using the features at each pixel location, which increases the computational cost in many cases. This approach is redundant because image statistics do not change significantly between neighboring image pixels. Furthermore, this redundancy may decrease tracking accuracy while tracking large targets because statistics of flat regions dominate region covariance matrix. In the proposed approach, salient points are extracted via the Shi and Tomasi’s minimum eigenvalue method over a Hessian matrix, and the RCD features extracted only at these salient points are used in target tracking. Experimental results indicate that the salient point RCD scheme provides comparable and even better tracking results compared to a classical RCD-based approach, scale-invariant feature transform, and speeded-up robust features-based trackers while providing a computationally more efficient structure.

Paper Details

Date Published: 22 February 2013
PDF: 13 pages
Opt. Eng. 52(2) 027207 doi: 10.1117/1.OE.52.2.027207
Published in: Optical Engineering Volume 52, Issue 2
Show Author Affiliations
Serdar Cakir, TÜBITAK National Research Institute of Electronics and Cryptology (Turkey)
Tayfun Aytaç, TÜBITAK National Research Institute of Electronics and Cryptology (Turkey)
Alper Yildirim, TÜBITAK National Research Institute of Electronics and Cryptology (Turkey)
Soosan Beheshti, Ryerson Univ. (Canada)
Oemer N. Gerek, Anadolu Univ. (Turkey)
Ahmet E. Cetin, Bilkent Univ. (Turkey)

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