Rule-Driven Object Tracking in Clutter and Partial Occlusion with Model-Based Snakes
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Rule-Driven Object Tracking in Clutter and Partial Occlusion with Model-Based Snakes Gabriel Tsechpenakis Center for Computational Biomedicine, Imaging and Modeling (CBIM), Division of Computer and Information Sciences, Rutgers University, NJ 08854, USA Email: [email protected]
Konstantinos Rapantzikos School of Electrical & Computer Engineering, National Technical University of Athens, Zografou, 15773 Athens, Greece Email: [email protected]
Nicolas Tsapatsoulis School of Electrical & Computer Engineering, National Technical University of Athens, Zografou, 15773 Athens, Greece Email: [email protected]
Stefanos Kollias School of Electrical & Computer Engineering, National Technical University of Athens, Zografou, 15773 Athens, Greece Email: [email protected] Received 5 February 2003; Revised 26 September 2003 In the last few years it has been made clear to the research community that further improvements in classic approaches for solving low-level computer vision and image/video understanding tasks are difficult to obtain. New approaches started evolving, employing knowledge-based processing, though transforming a priori knowledge to low-level models and rules are far from being straightforward. In this paper, we examine one of the most popular active contour models, snakes, and propose a snake model, modifying terms and introducing a model-based one that eliminates basic problems through the usage of prior shape knowledge in the model. A probabilistic rule-driven utilization of the proposed model follows, being able to handle (or cope with) objects of different shapes, contour complexities and motions; different environments, indoor and outdoor; cluttered sequences; and cases where background is complex (not smooth) and when moving objects get partially occluded. The proposed method has been tested in a variety of sequences and the experimental results verify its efficiency. Keywords and phrases: model-based snakes, rule-driven tracking, object partial occlusion.
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INTRODUCTION
In the last decade, snakes, a major category of active contours, have been given special attention in the fields of computer vision, image and video processing. They employ weak models, which deform in conformance with salient image features. The approaches proposed in the literature focus on either the highest accuracy of estimating moving silhouettes or the lowest computational complexity. Active contours (snakes) were first introduced by Kass et al. [1]. A snake is actually a curve defined by energy terms, being able to deform itself in order to minimize its total energy. This total energy consists of an “internal” term, that enforces smoothness along the curve, and an “external” term,
that makes the curve move towards the desired object boundaries. Many variations and extensions of snakes have been proposed and applied to certain applications [2, 3]. However, the majority of them faces three main limitations. The first one is the quality of the initialization that is crucial for the convergence of the algorithm. The second one
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