Advertisement

Video Object Contour Tracking Using Improved Dual-Front Active Contour

  • Qihe Li
  • Yuping Luo
  • Deyun Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

In this paper, we present an approach for moving object contour tracking in video by using an improved dual-front active contour model. Dual-front active contour model is first proposed for medical image segmentation. In order to adapt it to object tracking problem, we make two improvements on the original model. First, region force of the external front is modified by restricting its support region. This modification can speed up the algorithm greatly but may result in the active contour’s wrong convergence to the real object boundary when it locates in a large homogeneous region. Then, a new function called quasi-balloon force is brought into the model by modifying its active region construction method. It can not only solve the problem result from the first improvement but also make tracking more flexible. The algorithm does not need an a priori shape so it is fit for deformable object tracking. By adjusting the parameters, it can be used to track fast moving target. Since the level set method is used, the topology change of the object can be controlled automatically. And no static background of the scene is assumed which means the contour can be tracked under the condition that both the camera and the object are moving. Experimental results demonstrate its effectiveness and robustness.

Keywords

Active Contour Active Contour Model Initial Curve Support Region Geodesic Active Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qihe Li
    • 1
  • Yuping Luo
    • 1
  • Deyun Xiao
    • 1
  1. 1.Department of Automation, Tsinghua University, BeijingChina

Personalised recommendations