On active contour models and balloons
CVGIP: Image Understanding
International Journal of Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
A Multi-Label Front Propagation Approach for Object Segmentation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Tracking Multiple Objects through Occlusions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Local or global minima: flexible dual-front active contours
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
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.