Scale and Orientation-Based Background Weighted Histogram for Human Tracking

The Mean Shift procedure is a popular object tracking algorithm since it is fast, easy to implement and performs well in a range of conditions. However, classic Mean Shift tracking algorithm fixes the size and orientation of the tracking window, which limits the performance when the target’s orientation and scale change. In this paper, we present a new human tracking algorithm based on Mean Shift technique in order to estimate the position, scale and orientation changes of the target. This work combines moment features of the weight image with background information to design a robust tracking algorithm entitled Scale and Orientation-based Background Weighted Histogram (SOBWH). The experimental results show that the proposed approach SOBWH presents a good compromise between tracking precision and calculation time, also they validate its robustness, especially to large background variation, scale and orientation changes and similar background scenes.

[1]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

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

[3]  Wen Gao,et al.  Mean-Shift Blob Tracking with Adaptive Feature Selection and Scale Adaptation , 2007, 2007 IEEE International Conference on Image Processing.

[4]  Visvanathan Ramesh,et al.  Tunable Kernels for Tracking , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Hichem Snoussi,et al.  Improved mean shift integrating texture and color features for robust real time object tracking , 2012, The Visual Computer.

[6]  Qingchang Guo,et al.  Mean-Shift of Variable Window Based on the Epanechnikov Kernel , 2007, 2007 International Conference on Mechatronics and Automation.

[7]  B. Kröse,et al.  An EM-like algorithm for color-histogram-based object tracking , 2004, CVPR 2004.

[8]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognition Letters.

[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]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[11]  D. Zhang,et al.  Robust mean-shift tracking with corrected background-weighted histogram , 2012 .

[12]  Larry S. Davis,et al.  Efficient mean-shift tracking via a new similarity measure , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Zhongfei Zhang,et al.  A survey of appearance models in visual object tracking , 2013, ACM Trans. Intell. Syst. Technol..

[14]  Khalid Satori,et al.  Video-Surveillance System for Tracking Moving People Using Color Interest Points , 2014 .

[15]  Xiaohua Tian,et al.  Multi-scale mean shift tracking , 2015, IET Comput. Vis..

[16]  Ian D. Reid,et al.  Target tracking using mean-shift and affine structure , 2008, 2008 19th International Conference on Pattern Recognition.

[17]  Sharath Pankanti,et al.  Appearance models for occlusion handling , 2006, Image Vis. Comput..

[18]  D. Zhang,et al.  Scale and orientation adaptive mean shift tracking , 2012 .

[19]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[20]  P. Lauterbur,et al.  Principles of magnetic resonance imaging : a signal processing perspective , 1999 .

[21]  Khalid Satori,et al.  People Tracking using Color Control Points and Skin Color , 2014 .

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

[23]  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..

[24]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

[25]  Ning-Song Peng.  Automatic Selection of Kernel-Bandwidth for Mean-Shift Object Tracking , 2005 .

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

[27]  Peter Meer,et al.  Point matching under large image deformations and illumination changes , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Jwu-Sheng Hu,et al.  A spatial-color mean-shift object tracking algorithm with scale and orientation estimation , 2008, Pattern Recognit. Lett..

[29]  Abderrahim Saaidi,et al.  Human Tracking Based on Appearance Model , 2016 .

[30]  Shuxiao Li,et al.  Adaptive pyramid mean shift for global real-time visual tracking , 2010, Image Vis. Comput..

[31]  Richard J. Prokop,et al.  A survey of moment-based techniques for unoccluded object representation and recognition , 1992, CVGIP Graph. Model. Image Process..