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Showing posts with label Motion-Capture. Show all posts
Showing posts with label Motion-Capture. Show all posts

Friday, February 26, 2021

Real-Time Marker-Less Motion Capture for Animals

Real time feedback for animal movement and posture. 

Real time Studies of animal Motion by Neural activity.By EPFL (Switzerland)

Nik Papageorgiou, December 10, 2020

An updated deep learning software toolbox developed at the Swiss Federal Institute of Technology, Lausanne (EPFL) facilitates real-time feedback studies on animal movement and posture. DeepLabCut-Live! (DLC-Live!) is designed to enable computers to track and predict these factors free of motion-capture markers, by controlling or stimulating the animals' neural activity. DLC-Live!'s tailored networks predict posture from video frames, combined with low latency so researchers can supply real-time feedback and assess behavioral functions of specific neural circuits; the system also interfaces with hardware used in posture studies to deliver feedback to animals. EPFL's Mackenzie Mathis said, "It's economical, it's scalable, and we hope it's a technical advance that allows even more questions to be asked about how the brain controls behavior."   .. '

Tuesday, October 27, 2020

MonoEye - Human Motion Capture

 Most interesting approach.  The implications?  Note deep neural approach for selection of poses. Accuracy?  Gathering information for healthcare, sports, training?    Thinking possibilities.

MonoEye: A Human Motion-Capture System Using Single Wearable Camera

Tokyo Institute of Technology (Japan)

October 21, 2020

Researchers at Japan's Tokyo Institute of Technology and Carnegie Mellon University have developed a human motion-capture system comprised of an ultra-wide fisheye camera worn on the user's chest. The MonoEye system can capture the user's body motion and their perspective, or "viewport," with a 280-degree field of view. MonoEye incorporates three deep neural networks for real-time calculation of three-dimensional body pose, head pose, and camera pose. The researchers trained the networks on a synthetic dataset of 680,000 renderings of people with a range of body shapes, apparel, actions, background, and lighting conditions, along with 16,000 frames of photorealistic images. .... '