International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking and Compression for Video Communication
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
A Camera-Based System for Tracking People in Real Time
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Tracking using Color Correlogram
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
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Mean-Shift tracking gained a lot of popularity in computer vision community. This is due to its simplicity and robustness. However, the original formulation does not estimate the orientation of the tracked object. In this paper, we extend the original mean-shift tracker for orientation estimation. We use the gradient field as an orientation signature and introduce an efficient representation of the gradient-orientation space to speed-up the estimation. No additional parameter is required and the additional processing time is insignificant. The effectiveness of our method is demonstrated on typical sequences.