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Invariant Human Pose Feature Extract...

Peng, Bo.

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Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation./
Author:	Peng, Bo.
Description:	126 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
Contained By:	Dissertation Abstracts International72-07B.
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3453989
ISBN:	9781124626505

Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation.
Peng, Bo.

Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation. - 126 p.

Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .

Thesis (Ph.D.)--Arizona State University, 2011.

Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.

ISBN: 9781124626505Subjects--Topical Terms:

1669061
Engineering, Computer.

Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation.
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020 $a 9781124626505
035 $a (UMI)AAI3453989
035 $a AAI3453989
040 $a UMI $c UMI
100 1 $a Peng, Bo. $3 1681721
245 1 0 $a Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation.
300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
500 $a Adviser: Gang Qian.
502 $a Thesis (Ph.D.)--Arizona State University, 2011.
520 $a Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
590 $a School code: 0010.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Computer Science. $3 626642
690 $a 0464
690 $a 0544
690 $a 0984
710 2 $a Arizona State University. $b Electrical Engineering. $3 1671741
773 0 $t Dissertation Abstracts International $g 72-07B.
790 1 0 $a Qian, Gang, $e advisor
790 1 0 $a Ye, Jieping $e committee member
790 1 0 $a Li, Baoxin $e committee member
790 1 0 $a Spanias, Andreas $e committee member
790 $a 0010
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3453989