Study on Particle Filter Object Tracking Based on Weighted Fusion

2011 ◽  
Vol 403-408 ◽  
pp. 3049-3053
Author(s):  
Zhi Qiang Wen ◽  
Yan Hui Zhu ◽  
Zhao Yi Peng
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Linear Model ◽  
Information Fusion ◽  
Measurement Model ◽  
Color Histogram ◽  
Motion Information ◽  
Color Information ◽  
Model Based ◽  
Weighted Fusion

For the lack of self-adaptivity to environments, a measurement model based on weighted information fusion is presented in particle filter. Combined with color information and movement information of object, color histogram and motion Information histogram are built respectively, and then present a weighted linear model as the measurement model. The center-around method is adopted to compute the weight in the linear model. Lastly, a mass of experiments show the presented method be effective.

Particle-filter-based object tracking with color and texture information fusion

2009 ◽  
Author(s):  
RuiQing Chen ◽  
ZhaoHui Zhang ◽  
HanQing Lu ◽  
HuiQing Cui ◽  
YuKun Yan
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Information Fusion ◽  
Texture Information

An Improved Tracking Algorithm Combing Color and LBP Texture Features Based on Particle Filter

2013 ◽  
Vol 385-386 ◽  
pp. 1484-1487
Author(s):  
En Zeng Dong ◽  
Li Ya Su ◽  
Yan Hong Fu
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Reference Model ◽  
Texture Features ◽  
Tracking Algorithm ◽  
Color Histogram ◽  
Experimental Results ◽  
Single Feature ◽  
Highly Effective

In this paper, an tracking algorithm combing color and LBP texture features based on particle filter is proposed to overcome the disadvantages of existing particle filter object tracking methods. A color histogram and a texture histogram were combined to build the objects reference model, effectively improving the accuracy of object tracking. Experimental results demonstrate that, compared with the method based on single feature, the proposed method is highly effective, valid and is practicable.

Particle Filter Object Tracking Algorithm Based on Color and Gradient

2014 ◽  
Vol 926-930 ◽  
pp. 3141-3144 ◽  
Author(s):  
Jiai He ◽  
Yong Na Li
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Target Tracking ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Weighted Fusion ◽  
Illumination Changes ◽  
Particle Filter Algorithm

With the robustness of a single color which is not high in standard particle filter tracking, a fusion of color and gradient particle filter algorithm is proposed. By the advantages of color described the target ’global and gradients described the shape of structure, they are weighted fusion to form a new integrated histogram and applied to the particle filter framework. The experimental results show that compared with the traditional particle filter algorithm, the text of the algorithm can achieve relatively reliable target tracking under complicated background and illumination changes, with better robustness and reliability.

Single object tracking using particle filter framework and saliency-based weighted color histogram

2018 ◽  
Vol 77 (22) ◽  
pp. 30067-30088 ◽  
Author(s):  
Mai Thanh Nhat Truong ◽  
Myeongsuk Pak ◽  
Sanghoon Kim
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Color Histogram ◽  
Single Object

Object Tracking Algorithm Based on Block LAB Feature Histogram and Particle Filter

2012 ◽  
Vol 485 ◽  
pp. 193-199
Author(s):  
Ming Sun ◽  
Jia Wei Li
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Color Histogram ◽  
Complex Environments ◽  
Excellent Performance ◽  
Real Time Tracking ◽  
Tracking Objects

In order to improve real-time object tracking effect when tracking objects are partly covered or mixed by different backgrounds, and even under the conditions of changed illuminations, in this paper, we proposed an object tracking algorithm based on block LAB feature histogram and particle filter. To demonstrate new algorithm’s excellent performance, we carried several compared experiments when objects moved under different conditions such as changed illuminations, mixed backgrounds and so forth. Experiment results show that tracking objects are often lost by using tracking algorithm based on traditional features such as color histogram, but moving objects under various and complex environments could be correctly tracked by using real-time tracking algorithm proposed in this paper.

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