Skip to main content

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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Gabriel Tsechpenakis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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).

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: