Feature extraction for human action recognition based on saliency map

Human Action Recognition (HAR) plays an important role in computer vision for the interaction between human and environments which has been widely used in many applications. The focus of the research in recent years is the reliability of the feature extraction to achieve high performance with the us...

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Main Author:	Tan, Yi Ping
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79551/1/TanYiPingMFKE2018.pdf
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id	my-utm-ep.79551
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spelling	my-utm-ep.795512018-10-31T12:58:25Z Feature extraction for human action recognition based on saliency map 2018 Tan, Yi Ping TK Electrical engineering. Electronics Nuclear engineering Human Action Recognition (HAR) plays an important role in computer vision for the interaction between human and environments which has been widely used in many applications. The focus of the research in recent years is the reliability of the feature extraction to achieve high performance with the usage of saliency map. However, this task is challenging where problems are faced during human action detection when most of videos are taken with cluttered background scenery and increasing the difficulties to detect or recognize the human action accurately due to merging effects and different level of interest. In this project, the main objective is to design a model that utilizes feature extraction with optical flow method and edge detector. Besides, the accuracy of the saliency map generation is needed to improve with the feature extracted to recognize various human actions. For feature extraction, motion and edge features are proposed as two spatial-temporal cues that using edge detector and Motion Boundary Histogram (MBH) descriptor respectively. Both of them are able to describe the pixels with gradients and other vector components. In addition, the features extracted are implemented into saliency computation using Spectral Residual (SR) method to represent the Fourier transform of vectors to log spectrum and eliminating excessive noises with filtering and data compressing. Computation of the saliency map after obtaining the remaining salient regions are combined to form a final saliency map. Simulation result and data analysis is done with benchmark datasets of human actions using Matlab implementation. The expectation for proposed methodology is to achieve the state-of-art result in recognizing the human actions. 2018 Thesis http://eprints.utm.my/id/eprint/79551/ http://eprints.utm.my/id/eprint/79551/1/TanYiPingMFKE2018.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Tan, Yi Ping Feature extraction for human action recognition based on saliency map
description	Human Action Recognition (HAR) plays an important role in computer vision for the interaction between human and environments which has been widely used in many applications. The focus of the research in recent years is the reliability of the feature extraction to achieve high performance with the usage of saliency map. However, this task is challenging where problems are faced during human action detection when most of videos are taken with cluttered background scenery and increasing the difficulties to detect or recognize the human action accurately due to merging effects and different level of interest. In this project, the main objective is to design a model that utilizes feature extraction with optical flow method and edge detector. Besides, the accuracy of the saliency map generation is needed to improve with the feature extracted to recognize various human actions. For feature extraction, motion and edge features are proposed as two spatial-temporal cues that using edge detector and Motion Boundary Histogram (MBH) descriptor respectively. Both of them are able to describe the pixels with gradients and other vector components. In addition, the features extracted are implemented into saliency computation using Spectral Residual (SR) method to represent the Fourier transform of vectors to log spectrum and eliminating excessive noises with filtering and data compressing. Computation of the saliency map after obtaining the remaining salient regions are combined to form a final saliency map. Simulation result and data analysis is done with benchmark datasets of human actions using Matlab implementation. The expectation for proposed methodology is to achieve the state-of-art result in recognizing the human actions.
format	Thesis
qualification_level	Master's degree
author	Tan, Yi Ping
author_facet	Tan, Yi Ping
author_sort	Tan, Yi Ping
title	Feature extraction for human action recognition based on saliency map
title_short	Feature extraction for human action recognition based on saliency map
title_full	Feature extraction for human action recognition based on saliency map
title_fullStr	Feature extraction for human action recognition based on saliency map
title_full_unstemmed	Feature extraction for human action recognition based on saliency map
title_sort	feature extraction for human action recognition based on saliency map
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79551/1/TanYiPingMFKE2018.pdf
_version_	1747818254008582144

Feature extraction for human action recognition based on saliency map

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