Object Modeling with Color Arrangement for Region‐Based Tracking

In this paper, we propose a new color histogram model for object tracking. The proposed model incorporates the color arrangement of the target that encodes the relative spatial distribution of the colors inside the object. Using the color arrangement, we can determine which color bin is more reliable for tracking. Based on the proposed color histogram model, we derive a mean shift framework using a modified Bhattacharyya distance. In addition, we present a method of updating an object scale and a target model to cope with changes in the target appearance. Unlike conventional mean shift based methods, our algorithm produces satisfactory results even when the object being tracked shares similar colors with the background.

[1]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[2]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

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

[4]  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..

[5]  Ben J. A. Kröse,et al.  An EM-like algorithm for color-histogram-based object tracking , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  David A. Rottenberg,et al.  Quantitative comparison of four brain extraction algorithms , 2004, NeuroImage.

[7]  W. Clem Karl,et al.  Real-time tracking using level sets , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Stanley T. Birchfield,et al.  Spatiograms versus histograms for region-based tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Yanxi Liu,et al.  Online selection of discriminative tracking features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[12]  Stan Birchfield,et al.  Spatial Histograms for Region‐Based Tracking , 2007 .

[13]  Ruigang Yang,et al.  Spatial-Depth Super Resolution for Range Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Alper Yilmaz,et al.  Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Alan F. Smeaton,et al.  An Improved Spatiogram Similarity Measure for Robust Object Localisation , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[17]  W. Clem Karl,et al.  A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution , 2008, IEEE Transactions on Image Processing.

[18]  Yasushi Yagi,et al.  Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking , 2008, IEEE Transactions on Image Processing.

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

[20]  Hsi-Jian Lee,et al.  Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking , 2010 .

[21]  Fanglin Wang,et al.  Robust and efficient fragments-based tracking using mean shift , 2010 .