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Rule-Driven Object Tracking in Clutter and Partial Occlusion with Model-Based Snakes


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.

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Correspondence to Gabriel Tsechpenakis.

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Tsechpenakis, G., Rapantzikos, K., Tsapatsoulis, N. et al. Rule-Driven Object Tracking in Clutter and Partial Occlusion with Model-Based Snakes. EURASIP J. Adv. Signal Process. 2004, 812184 (2004).

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  • model-based snakes
  • rule-driven tracking
  • object partial occlusion