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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 672084, 15 pages
http://dx.doi.org/10.5402/2012/672084
Research Article

Model-Free, Occlusion Accommodating Active Contour Tracking

1Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8
2INRS-EMT, National Institute of Scientific Research, Montreal, QC, Canada H5A 1K6

Received 6 September 2012; Accepted 27 September 2012

Academic Editors: C. Kotropoulos and B. Schuller

Copyright © 2012 Mohamed Ben Salah and Amar Mitiche. 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 study investigates tracking in monocular image sequences by a model-free, occlusion accommodating active contour method. The objective functional contains a model-free shape tracking term to constrain the active curve in a frame to have a shape which approximates as closely as possible the shape of the active curve in the preceding frame. It complements a kernel photometric tracking term which constrains the active curve in a frame to enclose an intensity profile that matches as closely as possible the profile within the curve in the preceding frame. This data term is in kernel form so as to forgo image modeling. The method, which is exclusively driven by the curve/level set evolution equations derived from the objective functional Euler-Lagrange conditions, can track several objects independently. Experimental validation includes examples with infrared imaging, occlusion, clutter, and articulated motion.