Towards Solving the Missing Marker Problem in Realtime Motion Capture

A common problem in optical motion capture is the so-called missing marker problem. The occlusion of markers can lead to significant loss of tracking accuracy unless continuous data flow is guaranteed by computationally demanding interpolation or extrapolation schemes. Since interpolation algorithms require data sampled before and after an occlusion, they cannot be used for real-time applications. Extrapolation algorithms only require data sampled before an occlusion. Other algorithms require statistical data and are designed for post-processing. In order to bridge sampling gaps caused by occluded markers and hence to improve 3D real-time motion capture, we suggest a real-time extrapolation algorithm. The realization of this prediction algorithm does not need statistical data or rely on an underlying cinematic human model with pre-defined marker distances. Under the assumption that natural motion can be linear, circular, or a linear combination of both, a prediction method is suggested and realized. The paper presents linear and circular movement measurements for use when a marker is briefly lost. The suggested extrapolation method seems to behave well for a reasonable number of frames, not exceeding 200 milliseconds.

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A real-time motion capture framework for synchronized neural decoding

2011 IEEE International Symposium on VR Innovation ◽

10.1109/isvri.2011.5759656 ◽

2011 ◽

Cited By ~ 2

Author(s):

Guangming Lu ◽

Yi Li ◽

Shuai Jin ◽

Yang Zheng ◽

Weidong Chen ◽

...

Keyword(s):

Real Time ◽

Motion Capture ◽

Neural Decoding ◽

Time Motion

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Interactive Virtual Try-On Based on Real-Time Motion Capture

Advances in Multimedia Information Processing – PCM 2012 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-34778-8_58 ◽

2012 ◽

pp. 616-627 ◽

Cited By ~ 1

Author(s):

Xiaoyang Zhu ◽

Shuxin Qin ◽

Haitao Yu ◽

Shuiying Ge ◽

Yiping Yang ◽

...

Keyword(s):

Real Time ◽

Motion Capture ◽

Time Motion

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Real-Time Multi-person Motion Capture from Multi-view Video and IMUs

International Journal of Computer Vision ◽

10.1007/s11263-019-01270-5 ◽

2019 ◽

Vol 128 (6) ◽

pp. 1594-1611

Author(s):

Charles Malleson ◽

John Collomosse ◽

Adrian Hilton

Keyword(s):

Real Time ◽

Motion Capture ◽

Axial Rotation ◽

Time Motion ◽

Background Segmentation ◽

Measurement Units ◽

Multi Person Tracking ◽

Total Capture ◽

Optical Markers ◽

Indoor And Outdoor

AbstractA real-time motion capture system is presented which uses input from multiple standard video cameras and inertial measurement units (IMUs). The system is able to track multiple people simultaneously and requires no optical markers, specialized infra-red cameras or foreground/background segmentation, making it applicable to general indoor and outdoor scenarios with dynamic backgrounds and lighting. To overcome limitations of prior video or IMU-only approaches, we propose to use flexible combinations of multiple-view, calibrated video and IMU input along with a pose prior in an online optimization-based framework, which allows the full 6-DoF motion to be recovered including axial rotation of limbs and drift-free global position. A method for sorting and assigning raw input 2D keypoint detections into corresponding subjects is presented which facilitates multi-person tracking and rejection of any bystanders in the scene. The approach is evaluated on data from several indoor and outdoor capture environments with one or more subjects and the trade-off between input sparsity and tracking performance is discussed. State-of-the-art pose estimation performance is obtained on the Total Capture (mutli-view video and IMU) and Human 3.6M (multi-view video) datasets. Finally, a live demonstrator for the approach is presented showing real-time capture, solving and character animation using a light-weight, commodity hardware setup.

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Natural human–computer interaction control of multi operators in collaborative virtual maintenance based on optical human motion capture system

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962316500021 ◽

2016 ◽

Vol 07 (02) ◽

pp. 1650002

Author(s):

Xiangyang Li ◽

Zhili Zhang ◽

Feng Liang ◽

Qinhe Gao ◽

Lilong Tan

Keyword(s):

Human Computer Interaction ◽

Real Time ◽

Motion Capture ◽

Human Motion ◽

Motion Capture System ◽

Human Motion Capture ◽

Interaction Control ◽

Time Motion ◽

Virtual Maintenance ◽

Computer Interaction

Aiming at the human–computer interaction control (HCIC) requirements of multi operators in collaborative virtual maintenance (CVM), real-time motion capture and simulation drive of multi operators with optical human motion capture system (HMCS) is proposed. The detailed realization process of real-time motion capture and data drive for virtual operators in CVM environment is presented to actualize the natural and online interactive operations. In order to ensure the cooperative and orderly interactions of virtual operators with the input operations of actual operators, collaborative HCIC model is established according to specific planning, allocating and decision-making of different maintenance tasks as well as the human–computer interaction features and collaborative maintenance operation features among multi maintenance trainees in CVM process. Finally, results of the experimental implementation validate the effectiveness and practicability of proposed methods, models, strategies and mechanisms.

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Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action–effect anticipations during motor and social interactions

Behavior Research Methods ◽

10.3758/s13428-015-0580-5 ◽

2015 ◽

Vol 48 (1) ◽

pp. 366-380 ◽

Cited By ~ 6

Author(s):

Daniel Lewkowicz ◽

Yvonne Delevoye-Turrell

Keyword(s):

Social Interactions ◽

Open Source ◽

Real Time ◽

Motion Capture ◽

Source Code ◽

Action Effect ◽

Open Source Code ◽

Time Motion ◽

Effect Anticipations

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Research on the Application of Motion Capture of Kinect Technology in the Sport Training

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.2714 ◽

2014 ◽

Vol 926-930 ◽

pp. 2714-2717

Author(s):

Quan Wei Shi

Keyword(s):

Real Time ◽

Motion Capture ◽

The Body ◽

Sports Training ◽

The Real ◽

Sport Training ◽

Time Motion ◽

Development Costs ◽

Graphics Rendering ◽

Rendering Engine

For the real-time motion capture in the sport training to analysis and study, this paper adopts Kinect technology and the development of sports training combined with. Kinect somatosensory the camera as the system core, the body movements, facial expressions capture system in development costs, operating results and the development efficiency has the optimal balance point. The purpose of this research is based on the OGRE graphics rendering engine, using 3DSMAX and open source code, the design and implementation of Kinect somatosensory camera and 3DSMAX, OGRE combination of game action, motion capture system based on. This system provides an important help for realizing the real-time motion capture in the sports training, can be used in the field of sports training.

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

Malaysian Journal of Movement Health & Exercise ◽

10.15282/mohe.v7i1.185 ◽

2018 ◽

Vol 7 (1) ◽

Cited By ~ 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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