Particle Filtering Object Tracking Based on Texture and Color

Author(s):  
Hongwei Ying ◽  
Xuena Qiu ◽  
Jiatao Song ◽  
Xiaobo Ren
2019 ◽  
Vol 87 ◽  
pp. 112-124 ◽  
Author(s):  
Filiz Gurkan ◽  
Bilge Gunsel ◽  
Caner Ozer

2011 ◽  
Vol 341-342 ◽  
pp. 790-797 ◽  
Author(s):  
Zhi Yan Xiang ◽  
Tie Yong Cao ◽  
Peng Zhang ◽  
Tao Zhu ◽  
Jing Feng Pan

In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.


2005 ◽  
Author(s):  
Paul A. Brasnett ◽  
Lyudmila Mihaylova ◽  
Nishan Canagarajah ◽  
David Bull

Sign in / Sign up

Export Citation Format

Share Document