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Human Action Recognition from Variable Silhouette Energy Images

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dc.contributor.advisor Ahrnad, Prof. Dr. Mohiuddin
dc.contributor.author Parvin, Irine
dc.date.accessioned 2018-08-09T06:16:12Z
dc.date.available 2018-08-09T06:16:12Z
dc.date.copyright 2011
dc.date.issued 2011-06
dc.identifier.other ID 0000000
dc.identifier.uri http://hdl.handle.net/20.500.12228/245
dc.description This thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, June 2011. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 54-57).
dc.description.abstract Recognizing human actions is an important issue in the computer vision community. Human action recognition becomes more challenging when variability areas such as, anthropometric variation, phase variation, speed variation, camera view variaticn, individual variations in appearance and clothes of people, changes in light and view point and so on. In this thesis, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEl) and silhouette history image (SF11), to characterize motion and shape properties for recognition of humau movements such as, human actions, activities in daily life. We also proposed variable silhouette energy image for different variable situations. To address the variability in the recognition of human actions several parameters such as, anthropometry of person, phase (starting and ending state of action) speeds of the actions, camera observation (distance from camera, tilting motion and rotation of human body) and view variations are proposed. The SEI and Sill are constructed using the silhouette image sequence ofan action. The span or difference of the end time start time is used to make SF11. We extract the features based on geometrical shape moments. Using the features, we generate a unified description of model by learning (lie multi-class SVM for each action. Finally we recognize action using action model for any arbitrary image sequence. We tested our approach successfully in the indoor and outdoor environment. Our experimental results show that the proposed method is robust, flexible and efficient. en_US
dc.description.statementofresponsibility Irine parvin
dc.format.extent 58 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh. en_US
dc.rights Khulna University of Engineering & Technology (KUET) thesis/ dissertation/internship reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Human Action Recognition en_US
dc.subject Silhouette Energy Image en_US
dc.subject Silhouette History Image en_US
dc.title Human Action Recognition from Variable Silhouette Energy Images en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Electrical and Electronic Engineering
dc.contributor.department Department of Electrical and Electronic Engineering

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