Automatic markerless pose estimation for human in short sleeve or long sleeve attire

Markerless motion capture systems have the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Video analysis of human dynamics has a wide variety of applications in visual surveillance, visual-based human computer interaction, human robot interaction, gestu...

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Main Author:	Lim, Siew Hooi
Format:	Thesis
Language:	English
Published:	2008
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	https://eprints.ums.edu.my/id/eprint/10062/1/mt0000000371.pdf
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id	my-ums-ep.10062
record_format	uketd_dc
spelling	my-ums-ep.100622017-10-30T03:45:14Z Automatic markerless pose estimation for human in short sleeve or long sleeve attire 2008 Lim, Siew Hooi TA Engineering (General). Civil engineering (General) Markerless motion capture systems have the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Video analysis of human dynamics has a wide variety of applications in visual surveillance, visual-based human computer interaction, human robot interaction, gesture recognition and analysis, etc. Human pose estimation is a difficult problem, because human bodies are versatile, presenting a wide range of poses and self-occlusion occurrence. The goal of this project is to develop an automatic human pose estimation algorithm to recover the body and limb configurations in 20 from monocular video sequences without any special markers on the body. In this project, two approaches were described to model the human body poses from a monocular video sequence. In the first approach, a cardboard person model was proposed to model the upper part of human body using template matching algorithm. However silhouette feature alone was not enough to recover body configurations of the human when the body parts occluded each other and it was computationally expensive. Thus, a computer vision based approach was proposed as the second method to automatically detect human body parts and estimate the human body poses from a markerless monocular video sequence. The input image was first segmented using a silhouette extraction function based on the brightness level transformation to extract the moving silhouette patterns (human figures) from a static background for subsequent processing. Human body parts detection was then performed using colour, contours and silhouettes cues. Human body model initialization was performed in a fully automatic way. The only assumption was the person should be in an upright and frontal poses in the video sequence. Circular head fitting was first detected using circular Hough transform method. K-means clustering was then performed on the circular head region to obtain the skin colour distribution of the face. A skin colour model was built from the detected face region and used to find the candidate positions of limbs. The pixel classification performance was measured by using a receiver operating characteristic (ROC) curve. Radon transform was used to obtain more accurate orientation of the upper arms. Various physical and motion constraints regarding the human body were then used to construct the upper body configuration. The relation of the hand position inside the torso region was introduced to estimate the human pose for long sleeve attire's users. The final stage of this project was to recognize the pose performed by the human subject. Nineteen different body poses were considered for classification using feed-forward back propagation neural network, in which eight features are extracted from each pose. Our algorithm can recognize poses for any person entering the scene In either short sleeve or long sleeve shirt. It could also estimate the human poses even under illumination changes, self-occlusion occurrence and distance variations. There is no need for our system to use skin colour model built from skin pixel database for skin colour detection. Human pose estimation using computer vision-based approach reduces the computational cost by finding correct body part candidates more efficiently. 2008 Thesis https://eprints.ums.edu.my/id/eprint/10062/ https://eprints.ums.edu.my/id/eprint/10062/1/mt0000000371.pdf text en public masters Universiti Malaysia Sabah School of Engineering and Information Technology
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Lim, Siew Hooi Automatic markerless pose estimation for human in short sleeve or long sleeve attire
description	Markerless motion capture systems have the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Video analysis of human dynamics has a wide variety of applications in visual surveillance, visual-based human computer interaction, human robot interaction, gesture recognition and analysis, etc. Human pose estimation is a difficult problem, because human bodies are versatile, presenting a wide range of poses and self-occlusion occurrence. The goal of this project is to develop an automatic human pose estimation algorithm to recover the body and limb configurations in 20 from monocular video sequences without any special markers on the body. In this project, two approaches were described to model the human body poses from a monocular video sequence. In the first approach, a cardboard person model was proposed to model the upper part of human body using template matching algorithm. However silhouette feature alone was not enough to recover body configurations of the human when the body parts occluded each other and it was computationally expensive. Thus, a computer vision based approach was proposed as the second method to automatically detect human body parts and estimate the human body poses from a markerless monocular video sequence. The input image was first segmented using a silhouette extraction function based on the brightness level transformation to extract the moving silhouette patterns (human figures) from a static background for subsequent processing. Human body parts detection was then performed using colour, contours and silhouettes cues. Human body model initialization was performed in a fully automatic way. The only assumption was the person should be in an upright and frontal poses in the video sequence. Circular head fitting was first detected using circular Hough transform method. K-means clustering was then performed on the circular head region to obtain the skin colour distribution of the face. A skin colour model was built from the detected face region and used to find the candidate positions of limbs. The pixel classification performance was measured by using a receiver operating characteristic (ROC) curve. Radon transform was used to obtain more accurate orientation of the upper arms. Various physical and motion constraints regarding the human body were then used to construct the upper body configuration. The relation of the hand position inside the torso region was introduced to estimate the human pose for long sleeve attire's users. The final stage of this project was to recognize the pose performed by the human subject. Nineteen different body poses were considered for classification using feed-forward back propagation neural network, in which eight features are extracted from each pose. Our algorithm can recognize poses for any person entering the scene In either short sleeve or long sleeve shirt. It could also estimate the human poses even under illumination changes, self-occlusion occurrence and distance variations. There is no need for our system to use skin colour model built from skin pixel database for skin colour detection. Human pose estimation using computer vision-based approach reduces the computational cost by finding correct body part candidates more efficiently.
format	Thesis
qualification_level	Master's degree
author	Lim, Siew Hooi
author_facet	Lim, Siew Hooi
author_sort	Lim, Siew Hooi
title	Automatic markerless pose estimation for human in short sleeve or long sleeve attire
title_short	Automatic markerless pose estimation for human in short sleeve or long sleeve attire
title_full	Automatic markerless pose estimation for human in short sleeve or long sleeve attire
title_fullStr	Automatic markerless pose estimation for human in short sleeve or long sleeve attire
title_full_unstemmed	Automatic markerless pose estimation for human in short sleeve or long sleeve attire
title_sort	automatic markerless pose estimation for human in short sleeve or long sleeve attire
granting_institution	Universiti Malaysia Sabah
granting_department	School of Engineering and Information Technology
publishDate	2008
url	https://eprints.ums.edu.my/id/eprint/10062/1/mt0000000371.pdf
_version_	1747836312781586432

Automatic markerless pose estimation for human in short sleeve or long sleeve attire

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