Table of Contents
ISRN Machine Vision
Volume 2013, Article ID 863923, 14 pages
http://dx.doi.org/10.1155/2013/863923
Research Article

Deformable Contour-Based Maneuvering Flying Vehicle Tracking in Color Video Sequences

1Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
2Faculty of Engineering, Shahed University, Tehran 18651-33191, Iran
3School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14115-143, Iran

Received 23 October 2012; Accepted 11 December 2012

Academic Editors: A. Gasteratos, C.-C. Han, D. P. Mukherjee, A. Prati, and J. M. Tavares

Copyright © 2013 Samira Sabouri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a new method for the tracking of maneuvering flying vehicles using a deformable contour model in color video sequences. The proposed approach concentrates on targets with maneuvering motion in sky, which involves fundamental aspect change stemmed from 3D rotation of the target or video camera. In order to segment and track the aircraft in a video, at first, the target contour is initialized manually in a key frame, and then it is matched and tracked automatically in the subsequent frames. Generally active contour models employ a set of energy functions based on edge, texture, color, and shape features. Afterwards, objective function is minimized iteratively to track the target contour. In the proposed algorithm, we employ game of life cellular automaton to manage snake pixels’ (snaxels’) deformation in each epoch of minimization procedure. Furthermore, to cope with the large aspect change of aircraft, a Gaussian model has been taken into account to represent the target color in RGB space. To compensate for changes in luminance and chrominance ingredients of the target, the prior distribution function is dynamically updated during tracking. The proposed algorithm is evaluated using the collected dataset, and the expected probability of tracking error is calculated. Experimental results show positive results for the proposed algorithm.