Excogitation and Realization of Remote Chess Play System Based on Augmented Reality

2013 ◽  
Vol 756-759 ◽  
pp. 1788-1792
Author(s):  
Hui Bai Wang ◽  
Li Na Cui
Keyword(s):  
Distance Education ◽  
Augmented Reality ◽  
Medical Treatment ◽  
Real Time ◽  
Motion Detection ◽  
Mean Shift ◽  
3D Video ◽  
High Similarity ◽  
Mean Shift Algorithm ◽  
Remote Interaction

A remote chess play system based on augmented reality has been designed, and that could achieve two people face-to-faces playing chess but in fact the two people are separated by distance. The remote chess play system could be subdivided into six modules, and they are 3D video collected live, chessboard recognition, chess motion detection, compression and translation, virtual-real registration and virtual-real synthesis and 3D video played. The six modules have been introduced in details, and chessboard recognition and alternation of registration have been focused on in this paper. SSIFT-Mean Shift algorithm would be put forward, improving traditional Mean Shift algorithm when the target and background are under the condition of high similarity then the goals are easily lost. More important, real-time is enhanced. Currently the system has been used in practice and the method developed in the remote chess play system also can be applied to the remote medical treatment, distance education and remote interaction, etc.

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 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.

Research on target tracking algorithm based on mean shift with adaptive bandwidth

2021 ◽  
pp. 1-15
Author(s):  
Ming Han ◽  
Jingqin Wang ◽  
Jingtao Wang ◽  
Junying Meng ◽  
Ying Cheng
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Kernel Function ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Distance Constraint ◽  
Mean Shift Algorithm ◽  
Signed Distance ◽  
Adaptive Bandwidth ◽  
Anisotropic Kernel

The traditional mean shift algorithm used fixed kernels or symmetric kernel function, which will cause the target tracking lost or failure. The target tracking algorithm based on mean shift with adaptive bandwidth was proposed. Firstly, the signed distance constraint function was introduced to produce the anisotropic kernel function based on signed distance kernel function. This anisotropic kernel function satisfies that the value of the region function outside the target is zero, which provides accurate tracking window for the target tracking. Secondly, calculate the mean shift window center of anisotropic kernel function template, the theory basis is the sum of vector weights from the sample point in the tracking window to the center point is zero. Thirdly, anisotropic kernel function templates adaptive update implementation by similarity threshold to limit the change of the template between two sequential pictures, so as to realize real-time precise tracking. Finally, the contrast experimental results show that our algorithm has good accuracy and high real time.

scholarly journals Real-Time Depth-Based Hand Detection and Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Sung-Il Joo ◽  
Sun-Hee Weon ◽  
Hyung-Il Choi
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Region Growing ◽  
Tracking Accuracy ◽  
Hand Detection ◽  
Seed Point ◽  
Mean Shift Algorithm ◽  
Detection And Tracking ◽  
Cascade Structure ◽  
Hand Region

This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations.

An improved method for swing measurement based on monocular vision to the payload of overhead crane

2020 ◽  
pp. 014233122092131
Author(s):  
Jinling Huang ◽  
Weimin Xu ◽  
Weiwei Zhao ◽  
Hesong Yuan
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Mean Shift ◽  
Region Of Interest ◽  
Image Plane ◽  
Overhead Crane ◽  
Diagram Method ◽  
Mean Shift Algorithm ◽  
Swing Angle ◽  
Motion Speed

In order to solve the problem that the blurred image of a moving object decreases accuracy in the process of detecting the payload swing angle of an overhead crane based on vision, and the tracking failure caused by the drastic change of grey targets, a robust real-time detection method of the load swing angle of a bridge crane is proposed. This method uses a spherical marker attached to the load, which is insensitive to rotation and tilt when it is detected. First, it uses the mean shift algorithm combined with Kalman filter to track the moving objects in the image plane continuously, and then integrates the method of minimum area circle to detect the spherical marker image in the region of interest accurately and quickly. Finally, combined with the geometric method, the real-time swing angle is calculated. In addition, the angle diagram method is used to increase the speed of calculating the swing angle. The experimental results show that the method is effective for detecting the load target swing angle of different trolley motion speed.

Research on Application of Extended Mean-Shift Algorithm in Image Smoothing

2013 ◽  
Vol 457-458 ◽  
pp. 1126-1129
Author(s):  
De Li Zhu
Keyword(s):  
Image Segmentation ◽  
Object Tracking ◽  
Real Time ◽  
Mean Shift ◽  
Image Smoothing ◽  
Mean Shift Algorithm ◽  
Research On Application

This paper studied a method which can extend Mean-Shift algorithm based on Kernel. We describe its basic idea, given the specific steps of the algorithm. And given the application of extended Mean-Shift algorithm in the area of image smoothing. The research also show that extended Mean-Shift algorithm has displayed strong vitality in image segmentation and real-time object tracking.

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