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...

全面介紹

Saved in:
書目詳細資料
主要作者: Tan, Yi Ping
格式: Thesis
語言:English
出版: 2018
主題:
在線閱讀:http://eprints.utm.my/id/eprint/79551/1/TanYiPingMFKE2018.pdf
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
id my-utm-ep.79551
record_format uketd_dc
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