EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Video Analytics in Urban Environments
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A Class of Algorithms for Fast Digital Image Registration
IEEE Transactions on Computers
International Journal of Computer Vision
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Studentized Dynamical System for Robust Object Tracking
IEEE Transactions on Image Processing
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In this paper a visual object tracking method is presented which is robust against changes in the object appearance, shape, and scale. This method is also able to track objects being occluded temporarily in cluttered environments. It is assumed the target object moves freely through an unpredicted pattern in a dynamic environment where the camera may not be stationary. The proposed method models the object representation by an adaptive and deformable template which consists of several Gaussian functions. A 5 degree-of-freedom transformation function is employed to map the pixels from the template reference frame to the image reference frame. Moreover, the object localization method is based on a robust probabilistic optimization algorithm which is performed at every image frame to estimate the transformation parameters. The comparisons of the results obtained by the proposed tracker and several state-of-the-art methods with the manually labeled ground truth data demonstrate higher accuracy and robustness of the proposed method in this work.