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.

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.

A Tracking Window Adaptive Compressive Tracking Algorithm

2019 ◽  
Vol 33 (14) ◽  
pp. 1959045 ◽  
Author(s):  
Wenhao Wang ◽  
Mingxin Jiang ◽  
Xiaobing Chen ◽  
Li Hua ◽  
Shangbing Gao
Keyword(s):  
Texture Features ◽  
Candidate Region ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Scale Invariant ◽  
Compressive Tracking ◽  
Multi Scale ◽  
Feature Normalization ◽  
Object Region ◽  
Candidate Regions

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.

Pedestrian Tracking with HOG and Color Histogram Features

2012 ◽  
Vol 241-244 ◽  
pp. 498-501
Author(s):  
Lie Guo ◽  
Guang Xi Zhang ◽  
Ping Shu Ge ◽  
Lin Hui Li
Keyword(s):  
Particle Filter ◽  
Target Position ◽  
Color Histogram ◽  
Experimental Results ◽  
Pedestrian Tracking ◽  
Particle Sampling ◽  
Histograms Of Oriented Gradients ◽  
Initial Target

To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.

scholarly journals Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Liyan Chen ◽  
Beizhan Wang ◽  
Zhihong Zhang ◽  
Fan Lin ◽  
Yihan Ma
Keyword(s):  
Traditional Medicine ◽  
Detection Rate ◽  
Texture Features ◽  
Experimental Results ◽  
Chinese Traditional Medicine ◽  
Image Information ◽  
Novel Approach ◽  
Single Feature ◽  
Main Color ◽  
Tongue Color

Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease’s situation by observing patient’s tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improved GLA (Generalized Lloyd Algorithm) to extract the main color of tongue image. Considering that the color feature cannot fully express tongue image information, the paper analyzes tongue edge’s texture features and proposes an algorithm to extract them. Then, we integrate the two features in retrieval by different weight. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval.

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