Random decision tree body part recognition using FPGAs

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 ca...

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Bibliographic Details
Main Authors: Oberg, J., Eguro, K., Bittner, R., Forin, A.
Format: Conference Proceeding
Language:eng ; jpn
Subjects:
Bandwidth
Classification algorithms
Decision trees
Hardware
Memory management
Vegetation
Online Access:Request full text
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Summary: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.
ISSN:1946-147X
1946-1488
DOI:10.1109/FPL.2012.6339226