Salient point region covariance descriptor for target tracking

Serdar Cakir; Tayfun Aytaç; Alper Yildirim; Soosan Beheshti; Oemer N. Gerek; Ahmet E. Cetin

doi:10.1117/1.OE.52.2.027207

22 February 2013 Salient point region covariance descriptor for target tracking

Serdar Cakir, Tayfun Aytaç, Alper Yildirim, Soosan Beheshti, Oemer N. Gerek, Ahmet E. Cetin

Author Affiliations +

Optical Engineering, Vol. 52, Issue 2, 027207 (February 2013). https://doi.org/10.1117/1.OE.52.2.027207

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.

Citation Download Citation

Serdar Cakir, Tayfun Aytaç, Alper Yildirim, Soosan Beheshti, Oemer N. Gerek, and Ahmet E. Cetin "Salient point region covariance descriptor for target tracking," Optical Engineering 52(2), 027207 (22 February 2013). https://doi.org/10.1117/1.OE.52.2.027207

Published: 22 February 2013

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