Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Method for Tracking Objects with Rotation and Translation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Affine Object Tracking with Kernel-Based Spatial-Color Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Object Tracking using Color Correlogram
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Tree-structured image difference for fast histogram and distance between histograms computation
Pattern Recognition Letters
Hi-index | 0.10 |
In this paper, an enhanced mean-shift tracking algorithm using joint spatial-color feature and a novel similarity measure function is proposed. The target image is modeled with the kernel density estimation and new similarity measure functions are developed using the expectation of the estimated kernel density. With these new similarity measure functions, two similarity-based mean-shift tracking algorithms are derived. To enhance the robustness, the weighted-background information is added into the proposed tracking algorithm. Further, to cope with the object deformation problem, the principal components of the variance matrix are computed to update the orientation of the tracking object, and corresponding eigenvalues are used to monitor the scale of the object. The experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale changes.