{"id":630927,"date":"2020-01-13T15:23:10","date_gmt":"2020-01-13T23:23:10","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=630927"},"modified":"2020-04-29T17:24:09","modified_gmt":"2020-04-30T00:24:09","slug":"skeletal-tracking-on-azure-kinect","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/skeletal-tracking-on-azure-kinect\/","title":{"rendered":"Skeletal Tracking on Azure Kinect"},"content":{"rendered":"

Microsoft has released a new RGB-D sensor called Azure Kinect. In this project, we develop the skeletal tracking SDK for Azure Kinect. The product is called Azure Kinect Body Tracking SDK. It consists of 2D pose estimation and 3D model fitting. The 2D pose estimation is a neural network based solution and its input is the IR image of the depth sensor.<\/p>\n

Here<\/a> is a link to the Azure Kinect Body Tracking SDK which is free to download.<\/p>\n

Presentations:<\/p>\n

CVPR2019 Workshop on 3D Computer Vision in Medical Environments<\/a><\/p>\n

ICIP2019 Microsoft Industry Workshop – Machine Learning and Computer Vision Applications<\/p>\n

Slides <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Microsoft has released a new RGB-D sensor called Azure Kinect. I'm involved in developing the skeletral tracking for Azure Kinect. It consists of 2D pose estimation and 3D model fitting. The 2D pose estimation is a neural network based solution and its input is thje IR image of the depth sensor.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13556,13562],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-630927","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2019-09-01","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[],"msr_research_lab":[199565],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/630927"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":9,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/630927\/revisions"}],"predecessor-version":[{"id":643344,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/630927\/revisions\/643344"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=630927"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=630927"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=630927"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=630927"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=630927"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}