@inbook{cf29f132adc9441897d9025a55c29abe,
title = "Learning Randomized Decision Trees for Human Behavior Capture",
abstract = "This chapter focuses on recent research in Randomized Decision Tree (RDT) algorithm; in particular, how to classify human face and body using pixelwise classification of depth image. Similar to popular machine learning algorithms, training of RDT is also computation intensive. This chapter shows a more efficient technique to reduce the training time of the RDT algorithm. Hence, it is suitable for power-constrained devices. Besides, two applications are presented in this chapter to show the efficiency of the technique:(i) a fall detection system that monitor human fall down; (ii) ahuman-computer interface system that enable human to use nose and mouth to control computer mouse. The applications end with experimental results and performance evaluations.",
author = "Zhen-Peng Bian and Cheen-Hau Tan and Junhui Hou and Lap-Pui Chau",
note = "Research Unit(s) information for this publication is provided by the author(s) concerned.",
year = "2018",
month = nov,
day = "28",
language = "English",
isbn = "978-981-4800-51-8",
series = "Pan Stanford Series on Digital Signal Processing",
publisher = "Pan Stanford Publishing Pte. Ltd.",
pages = "435--470",
editor = "Wan-Chi Siu and Lap-Pui Chau and Liang Wang and Tieniu Tang",
booktitle = "Learning Approaches in Signal Processing",
edition = "1st",
}