@inproceedings{oberg2012random,
author = {Oberg, Jason and Eguro, Ken and Bittner, Ray and Forin, Alessandro},
title = {Random Decision Tree Body Part Recognition Using FPGAs},
booktitle = {International Conference on Field Programmable Logic and Applications},
year = {2012},
month = {August},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/random-decision-tree-body-part-recognition-using-fpgas/},
edition = {International Conference on Field Programmable Logic and Applications},
}