Coarse-to-Fine Feature Point Tracking Based on Combination of Feature Point Matching with Kalman-Filter and Mean-Shift Search

<Summary> Mean-Shift is a computationally low-cost method of an extremal search. In an application of feature point tracking, Mean-Shift search is sometimes not able to track the feature points because the search falls into a pitfall of an incorrect local solution when their points move widely and their features change significantly. We propose a new coarse-to-fine feature point tracking algorithm. Firstly, the Kalmanfilter based feature point matching corresponds coarsely the tracking point and the detected points in a wide-area. And then Mean-shift searches the feature point finely from the corresponding point in a narrowarea. Our method resolves a disadvantage while taking the advantage of Mean-Shift. We evaluated our method to track feature points for simulation movies and two kinds of sports movies. Our method tracked more feature points than other methods for a long time.

[1]  Matthew A. Brown,et al.  Recognising panoramas , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  Zhenhai Wang,et al.  A new approach for adaptive background object tracking based on Kalman filter and mean shift , 2013, RACS.

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

[5]  Robert T. Collins,et al.  Mean-shift blob tracking through scale space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Patrick Pérez,et al.  Robust tracking with motion estimation and local Kernel-based color modeling , 2007, Image Vis. Comput..

[7]  Azeddine Beghdadi,et al.  Vehicle Tracking by non-Drifting Mean-shift using Projective Kalman Filter , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[8]  Fei Su,et al.  Multiple faces tracking based on joint kernel density estimation and robust feature descriptors , 2009, 2009 IEEE International Conference on Network Infrastructure and Digital Content.

[9]  Dorin Comaniciu,et al.  Mean shift analysis and applications , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Tieniu Tan,et al.  Real-time hand tracking using a mean shift embedded particle filter , 2007, Pattern Recognit..

[11]  Takayuki Okatani,et al.  Object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithms , 2004, ICPR 2004.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[14]  Dorin Comaniciu,et al.  Mean shift and optimal prediction for efficient object tracking , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[15]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[16]  Huiyu Zhou,et al.  Object tracking using SIFT features and mean shift , 2009, Comput. Vis. Image Underst..

[17]  Zhiwei Zhu,et al.  Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination , 2002, Object recognition supported by user interaction for service robots.

[18]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[19]  Peng Hu,et al.  A New Target Tracking Scheme Based on Improved Mean Shift and Adaptive Kalman Filter , 2012 .

[20]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.