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.
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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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.leondumoulin.nl/language/television/water-rush.php
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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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Applying deep learning to motion capture with DeepLabCut
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