RIGID OBJECT TRACKING USING MEAN-SHIFT WITH ADAPTIVE KERNEL BANDWIDTH SELECTION

2006 ◽  
Vol 03 (02) ◽  
pp. 101-107
Author(s):  
JIEXIN PU ◽  
NINGSONG PENG ◽  
XINHAN HUANG
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Bandwidth Selection ◽  
Rigid Object ◽  
Mean Shift Algorithm ◽  
Scale Parameters ◽  
Affine Model ◽  
Backward Tracking ◽  
Adaptive Kernel ◽  
Estimate Method

The mean shift algorithm is an efficient way for object tracking. However, there is presently no clear mechanism for selecting kernel bandwidth while the object is changing in size. This paper presents a novel bandwidth selection method for mean shift based rigid object tracking. The kernel bandwidth is updated by discovering the scale parameters of the object's affine model that are estimated by using the correspondences between the corner object in two consecutive frames. The centroid of the object is registered by a special backward tracking method. Therefore, we can not only get translation parameter to simplify affine model but also improve the accuracy of finding corner correspondences. In addition, the M-estimate method is employed to reject mismatched pairs (outliers) so as to get better regression results. We have applied the proposed method to track vehicles changing in size with encouraging results.

Object Tracking Approach based on Mean Shift Algorithm

2013 ◽  
Vol 8 (3) ◽  
Author(s):  
Xiaojing Zhang ◽  
Yajie Yue ◽  
Chenming Sha
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Mean Shift Algorithm

Corrected Background-weighted Histogram Mean Shift Tracking Algorithm Based on Adaptive Bandwidth

2013 ◽  
Vol 401-403 ◽  
pp. 1543-1546
Author(s):  
Feng Liu ◽  
Chao Zhang ◽  
Xiao Pei Wu
Keyword(s):  
Probability Distribution ◽  
Object Tracking ◽  
Model Updating ◽  
Mean Shift ◽  
Spatial Localization ◽  
Target Localization ◽  
Invariant Moment ◽  
Adaptive Kernel ◽  
Weighted Model ◽  
Weighted Histogram

The CBWH (corrected background-weighted histogram) scheme can effectively reduce backgrounds interference in target localization. But it still has the problem of scale and spatial localization inaccuracy. To solve the above issues, we proposed a method which generates a color probability distribution by taking advantage of the targets salient features. In the binary image, we calculate the invariant moment and thus to resize the tracking window of the next frame. A simple background-weighted model updating method is adopted to adapt to the complex background in tracking. Experimental results show that the proposed algorithm improves the robustness of object tracking by self-adaptive kernel-bandwidth updating.

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