Review of Mean Shift Algorithm and its Improvements

This paper review‟s the origins of basic mean shift algorithm, as being a procedure which iteratively moves data points to the average of data points and its extension to the field of object tracking. Tracking of any given object forms integral part in surveillance, control and analysis applications. The video tracker presented here works on the principle of mean shift algorithm. However tracker is challenged when there tends to be low illumination, scaling, occlusions and multiple tracking. To tackle these problems, improvements are made in existing mean shift tracking algorithm of which a few are reviewed and studied.

[1]  Kalyan Kumar Hati,et al.  Review and improvement areas of mean shift tracking algorithm , 2014, The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014).

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

[3]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

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

[5]  Darren Caulfield,et al.  Mean-Shift Tracking for Surveillance: Evaluations and Enhancements , 2011 .

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

[7]  Amir Babaeian,et al.  Mean shift-based object tracking with multiple features , 2009, 2009 41st Southeastern Symposium on System Theory.

[8]  Fei Long,et al.  Adaptive kernel-bandwidth object tracking based on Mean-shift algorithm , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[9]  Rajbabu Velmurugan,et al.  Illumination invariant Mean-shift tracking , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[11]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

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

[14]  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).

[15]  Wei Liu,et al.  Improved mean shift algorithm for moving object tracking , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

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

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

[18]  Changyou Wang,et al.  An Adaptive Kernel Bandwidth Mean-Shift Target Tracking Algorithm , 2012, 2012 International Conference on Control Engineering and Communication Technology.