Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views
Pattern Recognition Letters
CONDENSATION—Conditional Density Propagation forVisual Tracking
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
Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three dimensional model-based tracking using texture learning and matching
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selecting Discriminative Tracking Features Using Particle Filter
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object tracking in image sequences using point features
Pattern Recognition
Feature selection for reliable tracking using template matching
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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The problem of object tracking in dense clutter is a challenge in computer vision. This paper proposes a method for tracking object robustly by combining the online selection of discriminative color features and the offline selection of discriminative Haar features. Furthermore, the cascade particle filter which has four stages of importance sampling is used to fuse two kinds of features efficiently. When the illumination changes dramatically, the Haar features selected offline play a major role. When the object is occluded, or its rotation angle is very large, the color features selected online play a major role. The experimental results show that the proposed method performs well under the conditions of illumination change, occlusion, object scale change and abrupt motion of object or camera.