Mean-Shift Guided Particle Filter Tracking Method Based on Layered Dynamic Template Update

2013 ◽  
Vol 846-847 ◽  
pp. 1217-1220
Author(s):  
Yuan Zheng Li
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Complex Scenes ◽  
One Step ◽  
The One ◽  
Dynamic Template ◽  
Update Strategy

Traditional tracking algorithm is not compatible between robustness and efficiency, under complex scenes, the stable template update strategy is not robust to target appearance changes. Therefore, the paper presents a dynamic template-update method that combined with a mean-shift guided particle filter tracking method. By incorporating the original information into the updated template, or according to the variety of each component in template to adjust the updating weights adaptively, the presented algorithm has the natural ability of anti-drift. Besides, the proposed method cope the one-step iteration of mean-shift algorithm with the particle filter, thus boost the performance of efficiency. Experimental results show the feasibility of the proposed algorithm in this paper.

scholarly journals Extended Target GMPHD Filter Based on Mean Shift and Graph Structure

2018 ◽  
Vol 36 (3) ◽  
pp. 420-425
Author(s):  
Xuan Cheng ◽  
Liping Song ◽  
Hongbing Ji
Keyword(s):  
Target Tracking ◽  
Mean Shift ◽  
Graph Structure ◽  
Matlab Simulation ◽  
Structure Information ◽  
Mean Shift Algorithm ◽  
Extended Target ◽  
One Step ◽  
The One ◽  
Computation Load

In view of excessive measurements partition number, a large computation load of extended target tracking and leakage estimation when the extended targets cross, an extended target tracking algorithm based on GMPHD with mean shift and graph structure is proposed. Firstly, the kernel density estimation is used to eliminate the clutter measurements. Secondly, mean shift algorithm is adopted to divide the extended target measurements set, and sub-division is considered to carry or not based on the information fed back from the updated graph structure. Then, the extended target GMPHD algorithm is used to filter. Finally, the graph structure is updated by the one-step predicted value of the filtering result, and the updated graph structure information is used to guide the measurement partition at the next moment. Matlab simulation shows that the algorithm proposed decreases largely the number of measurements partition, reduces the computational complexity, and solves the leakage estimation problem when the targets cross.

Robust Spatial-Color Feature with New Similarity Measure and Adaptive Template Update for Mean-Shift Tracking

2013 ◽  
Vol 321-324 ◽  
pp. 1021-1029
Author(s):  
Lu Rong Shen ◽  
Xia Bin Dong ◽  
Rui Tao Lu ◽  
Yong Bin Zheng ◽  
Xin Sheng Huang
Keyword(s):  
Object Tracking ◽  
Similarity Measure ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Tracking Accuracy ◽  
Color Feature ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Update Strategy

In this paper, we analyze the object tracking task of mean-shift algorithm. A spatial-color and similarity based mean-shift tracking algorithm is proposed. The spatial-color feature is used to replace the color histogram, and an enhanced algorithm is derived by adopting a new similarity measure. We also introduce Lucas-Kanade algorithm to design a template update strategy, propose a template update algorithm for mean-shift. Experimental results show that these two improved mean-shift tracking algorithms have high tracking accuracy and good robustness to the change of appearance of the object.

Study of Missile Radiator Predicting and Tracking Technology on the Basis of Kalman Filter and Mean-Shift Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 1584-1589
Author(s):  
Zhen Hui Xu ◽  
Bao Quan Mao ◽  
Li Xu ◽  
Jun Yan Zhao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Kalman Filtering ◽  
Mean Shift ◽  
Moving Target ◽  
Tracking Method ◽  
The Real ◽  
Mean Shift Algorithm ◽  
Compensation Function ◽  
Tracking Technology

In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved Mean-Shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.

Particle Filter and Mean Shift Tracking Method Based on Multi-feature Fusion

2010 ◽  
Vol 32 (2) ◽  
pp. 411-415 ◽  
Author(s):  
Yuan-zheng Li ◽  
Zhao-yang Lu ◽  
Quan-xue Gao ◽  
Jing Li
Keyword(s):  
Particle Filter ◽  
Feature Fusion ◽  
Mean Shift ◽  
Tracking Method

scholarly journals Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sijie Du ◽  
Hongxin Xu ◽  
Tianping Li
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Low Cost ◽  
Mean Shift ◽  
Video Tracking ◽  
Experimental Result ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Positioning Errors ◽  
Tracking Process

In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.

Adaboost Classification-Based Object Tracking Method for Sequence Images

2010 ◽  
Vol 44-47 ◽  
pp. 3902-3906
Author(s):  
Jie Jia ◽  
Yong Jun Yang ◽  
Yi Ming Hou ◽  
Xiang Yang Zhang ◽  
He Huang
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Image Sequence ◽  
Dynamic Scene ◽  
Initial Image ◽  
Tracking Method ◽  
Object Position ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Adaboost Classifier

An object tracking framework based on adaboost and Mean-Shift for image sequence was proposed in the manuscript. The object rectangle and scene rectangle in the initial image of the sequence were drawn and then, labeled the pixel data in the two rectangles with 1 and 0. Trained the adaboost classifier by the pixel data and the corresponding labels. The obtained classifier was improved to be a 5 class classifier and employed to classify the data in the same scene region of next image. The confidence map including 5 values was got. The Mean-Shift algorithm is performed in the confidence map area to get the final object position. The rectangles of object and background were moved to the new position. The object rectangle was zoomed by 5 percent to adapt the object scale changing. The process including drawing rectangle, training, classification, orientation and zooming would be repeated until the end of the image sequence. The experiments result showed that the proposed algorithm is efficient for nonrigid object orientation in the dynamic scene.

Adaptive mean shift and particle filter tracking method based on joint feature

2013 ◽  
Author(s):  
La Zhang ◽  
Yingyun Yang ◽  
Huabing Wang ◽  
Yansi Yang ◽  
Bo Liu
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Method
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