{"id":163299,"date":"2012-08-01T00:00:00","date_gmt":"2012-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/random-decision-tree-body-part-recognition-using-fpgas\/"},"modified":"2018-10-16T21:55:26","modified_gmt":"2018-10-17T04:55:26","slug":"random-decision-tree-body-part-recognition-using-fpgas","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/random-decision-tree-body-part-recognition-using-fpgas\/","title":{"rendered":"Random Decision Tree Body Part Recognition Using FPGAs"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Random decision tree classification is used in a variety of applications, from speech recognition to Web search engines. Decision trees are used in the Microsoft Kinect vision pipeline to recognize human body parts and gestures for a more natural computer-user interface. Tree-based classification can be taxing, both in terms of computational load and memory bandwidth. This makes highly-optimized hardware implementations attractive, particularly given the strict power and form factor limitations of embedded or mobile platforms. In this paper we present a complete architecture that interfaces the Kinect depth-image sensor to an FPGA-based implementation of the Forest Fire pixel classification algorithm. Key performance parameters, algorithmic improvements and design trade-off are discussed.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Random decision tree classification is used in a variety of applications, from speech recognition to Web search engines. Decision trees are used in the Microsoft Kinect vision pipeline to recognize human body parts and gestures for a more natural computer-user interface. Tree-based classification can be taxing, both in terms of computational load and memory bandwidth. 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