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Proceedings Paper

A complete system for head tracking using motion-based particle filter and randomly perturbed active contour
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Paper Abstract

Recent advances in multimedia and communication require techniques for accurately tracking objects in video sequences. We propose a complete system for head tracking and contour refinement. Our tracking approach is based on particle filtering framework. However, unlike existing methods that use prior knowledge or likelihood functions as proposal densities, we use a motion-based proposal. Adaptive Block Matching (ABM) algorithm is the motion estimation technique used. Several advantages arise from this choice of proposal. (i) Only few samples are propagated. (ii) The tracking is adaptive to different categories of motion (iii) Off-line motion learning is not needed. Following the tracking is the contour refinement step. We want to transform the parametric estimate representing the tracked head at a given time instant into an elastic contour delineating the head’s boundaries. We use an active contour framework based on a dynamic programming scheme. However active contours are very sensitive to parameter assignment and initial condition. Using the tracked parametric estimate, we create a set of randomly perturbed initial conditions. The optimal contour is then the one corresponding to the lowest energy. Our system demonstrates tracking a person’s head in complex environments and delineates its boundaries for future use.

Paper Details

Date Published: 14 March 2005
PDF: 10 pages
Proc. SPIE 5685, Image and Video Communications and Processing 2005, (14 March 2005); doi: 10.1117/12.587244
Show Author Affiliations
N. Bouaynaya, Univ. of Illinois/Chicago (United States)
Dan Schonfeld, Univ. of Illinois/Chicago (United States)


Published in SPIE Proceedings Vol. 5685:
Image and Video Communications and Processing 2005
Amir Said; John G. Apostolopoulos, Editor(s)

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