Learning Randomized Decision Trees for Human Behavior Capture

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)

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Detail(s)

Original languageEnglish
Title of host publicationLearning Approaches in Signal Processing
EditorsWan-Chi Siu, Lap-Pui Chau, Liang Wang, Tieniu Tang
PublisherPan Stanford Publishing Pte. Ltd.
Chapter12
Pages435-470
Edition1st
ISBN (Electronic)978-0-429- 06114-1
ISBN (Print)978-981-4800-51-8
Publication statusPublished - 28 Nov 2018

Publication series

NamePan Stanford Series on Digital Signal Processing
Volume2

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.

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Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Learning Randomized Decision Trees for Human Behavior Capture. / Bian, Zhen-Peng; Tan, Cheen-Hau; Hou, Junhui; Chau, Lap-Pui.

Learning Approaches in Signal Processing. ed. / Wan-Chi Siu; Lap-Pui Chau; Liang Wang; Tieniu Tang. 1st. ed. Pan Stanford Publishing Pte. Ltd., 2018. p. 435-470 (Pan Stanford Series on Digital Signal Processing; Vol. 2).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)