Real-Time Motion Tracking Detection System for a Spherical Pendulum Using a USB Camera

2016 ◽  
Vol 40 (9) ◽  
pp. 807-813
Author(s):  
Byung-Yoon Moon ◽  
Sung-Rak Hong ◽  
Manh-Tuan Ha ◽  
Chul-Goo Kang
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Detection System ◽  
Spherical Pendulum ◽  
Time Motion ◽  
Usb Camera

Real-time motion tracking in image-guided oral implantology

2008 ◽  
Vol 4 (4) ◽  
pp. 339-347 ◽  
Author(s):  
Xiaojun Chen ◽  
Yanping Lin ◽  
Yiqun Wu ◽  
Chengtao Wang
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Image Guided ◽  
Time Motion ◽  
Oral Implantology

scholarly journals WFMHT Method of Orbit Maneuver Detection Based on Space-Based Bearing-Only Measurement

2018 ◽  
Vol 36 (6) ◽  
pp. 1185-1192
Author(s):  
Lei Liu ◽  
Jianfeng Cao ◽  
Ye Liu
Keyword(s):  
Real Time ◽  
Situation Awareness ◽  
Detection System ◽  
Hypothesis Test ◽  
Test Methods ◽  
Time Motion ◽  
Weighted Fusion ◽  
Innovation Characteristics ◽  
Detection Window ◽  
Time Awareness

The method of orbit maneuver detection of space targets is investigated using the space-based bearing-only measurement, which aims to acquire a real-time or nearly real-time awareness of orbit maneuver in the space situation awareness. First, the model for estimating real-time motion of a space target is presented, which only uses the space-based bearing-only measurements. The innovation characteristics of the normal orbit and orbit maneuver are analyzed and compared. Second, based on the hypothesis test methods of the distribution characteristic of the stochastic sequence, the WFMHT (i.e., weighted fusion of multi hypothesis tests) method with the innovation is put forward to detect the orbit maneuver. Furthermore, the criterion of determining the weight coefficients is studied. Finally, the method is validated by numeric simulations. The results show that the highest gained success rate is up to 36% with the WFMHT method than the prevalent Chi2 method. With the WFMHT method, the detection system achieves a strengthened robustness with greatly shortened detection window. The research will be beneficial to construction of our space situation awareness system.

Combination of simple vision modules for robust real-time motion tracking

1995 ◽  
Vol 6 (3) ◽  
pp. 275-286 ◽  
Author(s):  
Joachim Denzier ◽  
Heinrich Niemann
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Time Motion

scholarly journals Application of deep learning in automatic detection of technical and tactical indicators of table tennis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0245259
Author(s):  
Fufeng Qiao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Automatic Detection ◽  
Table Tennis ◽  
Time Dynamic ◽  
Time Motion ◽  
Automatic Detection System

A DCNN-LSTM (Deep Convolutional Neural Network-Long Short Term Memory) model is proposed to recognize and track table tennis’s real-time trajectory in complex environments, aiming to help the audiences understand competition details and provide a reference for training enthusiasts using computers. Real-time motion features are extracted via deep reinforcement networks. DCNN tracks the recognized objects, and the LSTM algorithm predicts the ball’s trajectory. The model is tested on a self-built video dataset and existing systems and compared with other algorithms to verify its effectiveness. Finally, an overall tactical detection system is built to measure ball rotation and predict ball trajectory. Results demonstrate that in feature extraction, the Deep Deterministic Policy Gradient (DDPG) algorithm has the best performance, with a maximum accuracy rate of 89% and a minimum mean square error of 0.2475. The accuracy of target tracking effect and trajectory prediction is as high as 90%. Compared with traditional methods, the performance of the DCNN-LSTM model based on deep learning is improved by 23.17%. The implemented automatic detection system of table tennis tactical indicators can deal with the problems of table tennis tracking and rotation measurement. It can provide a theoretical foundation and practical value for related research in real-time dynamic detection of balls.

scholarly journals Evaluation of real time motion tracking accuracy of customised IMU sensor for application in a mobile badminton virtual reality training system

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Zahari Taha ◽  
Mohd Yashim Wong ◽  
Hwa Jen Yap ◽  
Amirul Abdullah ◽  
Wee Kian Yeo
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Tracking ◽  
Training System ◽  
Microsoft Kinect ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Improve Performance ◽  
Motion Data ◽  
Time Motion

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.

Sign in / Sign up

Export Citation Format

Share Document