Tracking and data association
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Capture and representation of human walking in live video sequences
IEEE Transactions on Multimedia
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
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Shape tracking with low level features (e.g., edge points) often fails in complex environments (e.g., in the presence of clutter, inner edges, or multiple objects). Two alternative methods are discussed in this paper. Both methods use middle level features (data centroids, strokes) which are more informative and reliable than edge transitions used in most tracking algorithms. Furthermore, it is assumed in this paper that each feature can be either a valid measurement or an outlier. A confidence degree is assigned to each feature or to a given interpretation of all visual features. Features/ interpretations with high degrees of confidence have large influence on the shape estimates while features/interpretations with low degrees of confidence have negligible influence on the shape estimates. It is shown in this paper that both items (middle level features and confidence degrees) lead to a significant improvement of the tracker robustness and performance in the presence of clutter and abrupt shape and motion changes.