Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique

One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store,...
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Gespeichert in:

Autor*in:	Sebastien Mambou [verfasserIn] Ondrej Krejcar [verfasserIn] Kamil Kuca [verfasserIn] Ali Selamat [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	action recognition perspective sample-affinity matrix cross-view actions NUMA IXMAS

Übergeordnetes Werk:	In: Future Internet - MDPI AG, 2010, 10(2018), 9, p 89
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:9, p 89

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/fi10090089

Katalog-ID:	DOAJ010854975

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callnumber-first	T - Technology
author	Sebastien Mambou
spellingShingle	Sebastien Mambou misc T58.5-58.64 misc action recognition misc perspective misc sample-affinity matrix misc cross-view actions misc NUMA misc IXMAS misc Information technology Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique
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title	Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique
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title_auth	Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique
abstract	One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.
abstractGer	One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.
abstract_unstemmed	One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.
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title_short	Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique
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Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique

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