Open Access

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

  • Gabriel Tsechpenakis1Email author,
  • Konstantinos Rapantzikos2,
  • Nicolas Tsapatsoulis2 and
  • Stefanos Kollias2
EURASIP Journal on Advances in Signal Processing20042004:812184

DOI: 10.1155/S1110865704401103

Received: 5 February 2003

Published: 15 June 2004


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.


model-based snakes rule-driven tracking object partial occlusion

Authors’ Affiliations

Center for Computational Biomedicine, Imaging and Modeling (CBIM), Division of Computer and Information Sciences, Rutgers University
School of Electrical & Computer Engineering, National Technical University of Athens


© Tsechpenakis et al. 2004