Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Real-time tracking of image regions with changes in geometry and illumination
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Background Layer Model for Object Tracking Through Occlusion
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking Using Foreground-Background Texture Discrimination
International Journal of Computer Vision
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough-based tracking of non-rigid objects
Computer Vision and Image Understanding
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Model updating is a critical problem in tracking. Inaccurate extraction of the foreground and background information in model adaptation would cause the model to drift and degrade the tracking performance. The most direct but yet difficult solution to the drift problem is to obtain accurate boundaries of the target. We approach such a solution by proposing a novel closed-loop model adaptation framework based on the combination of matting and tracking. In our framework, the scribbles for matting are all automatically generated, which makes matting applicable in a tracking system. Meanwhile, accurate boundaries of the target can be obtained from matting results even when the target has large deformation. An effective model is further constructed and successfully updated based on such accurate boundaries. Extensive experiments show that our closed-loop adaptation scheme largely avoids model drift and significantly outperforms other discriminative tracking models as well as video matting approaches.