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Optical Engineering

Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets
Author(s): Serdar Cakir; Tayfun Aytaç; Alper Yildirim; Oemer N. Gerek
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Paper Abstract

An offline feature selection and evaluation mechanism is used in order to develop a robust visual tracking scheme for sea-surface and aerial targets. The covariance descriptors, known to constitute an efficient signature set in object detection and classification problems, are used in the feature extraction phase of the proposed scheme. The performance of feature sets are compared using support vector machines, and those resulting in the highest detection performance are used in the covariance based tracker. The tracking performance is evaluated in different scenarios using different performance measures with respect to ground truth target positions. The proposed tracking scheme is observed to track sea-surface and aerial targets with plausible accuracies, and the results show that gradient-based features, together with the pixel locations and intensity values, provide robust target tracking in both surveillance scenarios. The performance of the proposed tracking strategy is also compared with some well-known trackers including correlation, Kanade-Lucas-Tomasi feature, and scale invariant feature transform-based trackers. Experimental results and observations show that the proposed target tracking scheme outperforms other trackers in both air and sea surveillance scenarios.

Paper Details

Date Published: 1 October 2011
PDF: 14 pages
Opt. Eng. 50(10) 107205 doi: 10.1117/1.3640826
Published in: Optical Engineering Volume 50, Issue 10
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)
Oemer N. Gerek, Anadolu Univ. (Turkey)

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