scholarly journals Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

2013 ◽  
Author(s):  
Yu Yang ◽  
Yongxing Jia ◽  
Chuanzhen Rong ◽  
Ying Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Weighted Histogram

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

2013 ◽  
Vol 765-767 ◽  
pp. 720-725 ◽  
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Ying Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Complex Environments ◽  
The Real ◽  
Real Environment ◽  
Background Clutter ◽  
Weighted Histogram

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

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.

Sign in / Sign up

Export Citation Format

Share Document