Is the Hand really quicker than the Eye? Variances of the Mean-Shift algorithm for real-time hand and face tracking

2006 ◽  
Author(s):  
Farhad Dadgostar ◽  
Abdolhossein Sarrafzadeh ◽  
Scott P. Overmyer
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Face Tracking ◽  
Mean Shift Algorithm ◽  
The Mean

The mean shift algorithm and its relation to kernel regression

2016 ◽  
Vol 348 ◽  
pp. 198-208 ◽  
Author(s):  
Youness Aliyari Ghassabeh ◽  
Frank Rudzicz
Keyword(s):  
Mean Shift ◽  
Kernel Regression ◽  
Mean Shift Algorithm ◽  
The Mean

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

AN AUTONOMOUS SYSTEM FOR GROUND-BASED CLOUD TRACKING USING THE MEAN-SHIFT ALGORITHM

2011 ◽  
Author(s):  
M.D. O’Toole ◽  
S.A. Wormald ◽  
D. Kerr ◽  
J. Coupland ◽  
A.P. Sandford
Keyword(s):  
Autonomous System ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Cloud Tracking

Research on Face Tracking in Community Monitoring System

2014 ◽  
Vol 484-485 ◽  
pp. 358-362
Author(s):  
Shuang Liu
Keyword(s):  
Target Tracking ◽  
Skin Color ◽  
Mean Shift ◽  
Texture Features ◽  
Mean Shift Algorithm ◽  
Local Character ◽  
Community Monitoring ◽  
The Mean ◽  
Target Block ◽  
Time Update

This paper proposes a block Mean-Shift algorithm based on target real-time update and LBP texture features, through the target update improves the accuracy of target tracking, enhances the local character of the target through the target block, so as to improve the robustness of algorithm based on skin color backgrounds. And then analyze the Mean-Shift algorithm cannot recover quickly lost target tracking defects, and its improvement by combining the frame difference method.

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.

scholarly journals Thalamus Segmentation from Diffusion Tensor Magnetic Resonance Imaging

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Ye Duan ◽  
Xiaoling Li ◽  
Yongjian Xi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Mean Shift ◽  
Fixed Number ◽  
Resonance Imaging ◽  
Mean Shift Algorithm ◽  
Nuclei Segmentation ◽  
Segmentation Algorithms ◽  
The Mean

We propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift based algorithm supports hierarchical clustering naturally.

scholarly journals Motion Capture and Intelligent Correction Method of Badminton Movement Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yibo Zhang ◽  
Jianjun Tang ◽  
Hui Huang
Keyword(s):  
Machine Vision ◽  
Target Tracking ◽  
Motion Capture ◽  
Mean Shift ◽  
Weak Links ◽  
Bottom Line ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Combined Algorithm ◽  
Improved Algorithm

In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.

scholarly journals A High-Precision, Real-Time, and Robust Indoor Visible Light Positioning Method Based on Mean Shift Algorithm and Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1094 ◽  
Author(s):  
Zekun Xie ◽  
Weipeng Guan ◽  
Jieheng Zheng ◽  
Xinjie Zhang ◽  
Shihuan Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Light Emitting Diode ◽  
Mean Shift ◽  
Indoor Navigation ◽  
Positioning Accuracy ◽  
Mean Shift Algorithm

Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.

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