Robust foveal wavelet-based object tracking

In this work, a foveal wavelet-based Mean Shift Tracking Algorithm is presented. The foveal wavelets introduced by Mallat [16] are known by their high capability to precisely characterize the holder regularity of singularities. Therefore, by using the foveal wavelet transform, image features are accurately identified and are well discriminated from noise. These wavelets are used to extract the texture features of the target object. The extracted features are then used to construct a joint color-foveal textures histogram to represent the target object. Once the joint histogram is obtained, it is applied to the mean shift framework in order to track a target object in a video sequence. The experimental results showed that the proposed approach overcomes the traditional mean shift tracking technique as well as other existing tracking algorithms.

[1]  Tieniu Tan,et al.  Real time hand tracking by combining particle filtering and mean shift , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[2]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  David Zhang,et al.  Robust Object Tracking Using Joint Color-Texture Histogram , 2009, Int. J. Pattern Recognit. Artif. Intell..

[4]  Shengtong Zhong,et al.  Hand Tracking by Particle Filtering with Elite Particles Mean Shift , 2008, 2008 Japan-China Joint Workshop on Frontier of Computer Science and Technology.

[5]  Ming Zhu,et al.  Mean shift tracking combining SIFT , 2008, 2008 9th International Conference on Signal Processing.

[6]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[7]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[8]  Myron Flickner,et al.  Detection and tracking of shopping groups in stores , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[10]  Zhiyong Huang,et al.  Kernel-based method for tracking objects with rotation and translation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[11]  Irene Y. H. Gu,et al.  Joint anisotropic mean shift and consensus point feature correspondences for object tracking in video , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Irene Y. H. Gu,et al.  Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[14]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[15]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[16]  David W. Murray,et al.  Video-rate localization in multiple maps for wearable augmented reality , 2008, 2008 12th IEEE International Symposium on Wearable Computers.

[17]  Weimin Huang,et al.  Kernel-based method for tracking objects with rotation and translation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[18]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..