scholarly journals Real-time Motion Tracking from a Mobile Robot

2009 ◽  
Vol 2 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Boyoon Jung ◽  
Gaurav S. Sukhatme
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Motion Tracking ◽  
Time Motion

scholarly journals Real-time Motion Tracking from a Mobile Robot

2005 ◽  
Author(s):  
Boyoon Jung ◽  
Gaurav S. Sukhatme
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Motion Tracking ◽  
Time Motion

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

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

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 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.

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