Guide Markerless Motion Capture from Monocular Videos

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  1. Markerless Motion Capture from Monocular Videos
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  3. Figure 5 from Markerless Motion Capture from Monocular Videos - Semantic Scholar
  4. Applying deep learning to motion capture with DeepLabCut

Markerless Motion Capture from Monocular Videos

During the last two decades there has been much work in markerless human motion capture. This thesis contributes to the existing body of work by providing three new algorithms. First, an appearance descriptor is proposed for human pose estimation from monocular images. Second, a discriminative learning-based fusion algorithm is proposed to combine shape and appearance features for human pose estimation from monocular images. Third, a hybrid discriminative and generative method that takes into account prediction uncertainty of the discriminative model is proposed for 3D human pose tracking from both single and multiple cameras.

Shape-based features such as silhouettes and appearance features are commonly used for pose estimation from monocular images using regression based techniques.

MonoPerfCap: Human Performance Capture from Monocular Video - ACM TOG (presented SIGGRAPH 2018)

Silhouette features require a segmentation step to obtain only information pertinent to the shape of the occluding body parts and discards appearance information that can potentially be useful for pose estimation. AB - [Truncated abstract] Vision-based human pose estimation and tracking is a popular research area that has generated a great deal of interest in the last decade.

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WO2010129721A2 - Distributed markerless motion capture - Google Patents

Outcome Measures. Eligibility Criteria. Information from the National Library of Medicine Choosing to participate in a study is an important personal decision. Exclusion Criteria: Patients with physical disability i. Contacts and Locations. Information from the National Library of Medicine To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.

Related Stories

More Information. Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: proof of concept. PLoS One. Parkinson's disease assessment based on gait analysis using an innovative RGB-D camera system. Validation of a motion capture system for deriving accurate ground reaction forces without a force plate. Big Data Anal.

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Figure 5 from Markerless Motion Capture from Monocular Videos - Semantic Scholar

Moodie P. Lenexa, Kansas. The long term repeatability of a 3D markerless motion capture system and the implications it has on healthcare.


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J Appl Hum Mov. Repeatability of 3D markerless motion capture and how it could affect between-session variability. Validation of a markerless motion capture system for the calculation of lower extremity kinematics. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. Biomed Eng Online. Comparison of lower limb and trunk kinematics between markerless and marker-based motion capture systems.

Gait Posture.

Applying deep learning to motion capture with DeepLabCut

Epub Oct Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. Epub Jan Spatiotemporal gait parameters for patients with Parkinson's disease compared with normal individuals. Physiother Res Int. Epub Dec Effects of bilateral subthalamic stimulation on gait kinematics and kinetics in Parkinson's disease. Exp Brain Res. Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease. J Neurol Sci. Epub Jul Independent influence of gait speed and step length on stability and fall risk. National Library of Medicine U.

National Institutes of Health U. Department of Health and Human Services. The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Parkinson Disease. Not Applicable. Study Type :. Actual Enrollment :.