CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Shape based appearance model for kernel tracking
Image and Vision Computing
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
An interactive personalized video summarization based on sketches
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
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To overcome the shortages of SIFT features and Camshift to individually apply to object tracking, in this paper, an algorithm is presented which integrates SIFT and Camshift tracking. In the proposed algorithm, Bhattacharyya coefficient is used as an indicator that judges whether the matching result is stable or not. Then, SIFT and Camshift tracking can adaptively switch tracking method. The SIFT features of object are updated in real-time according to matched result and historical information. Experimental results demonstrate that this algorithm can track the object accurately in conditions of scale modifications, rotation, abrupt shifts, as well as clutter and partial occlusions occurring to the tracking object with good robustness.