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Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors
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Bester Preis: Fr. 14.65 (€ 14.99)¹ (vom 01.03.2018)Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors (2018)
ISBN: 9783668642607 bzw. 3668642605, in Deutsch, Grin Verlag; Grin Verlag, gebundenes Buch, neu.
Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces. The problem addressed in this paper is to explore different Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces. The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified. In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatial and temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance. Versandfertig in 3-5 Tagen Lieferzeit 1-2 Werktage.
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors (2018)
ISBN: 9783668642591 bzw. 3668642591, vermutlich in Englisch, GRIN Verlag, neu, E-Book, elektronischer Download.
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors: Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces. The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified. In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatial and temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance. Englisch, Ebook.
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors (2017)
ISBN: 9783668642607 bzw. 3668642605, in Deutsch, GRIN Verlag, Taschenbuch, neu, Nachdruck.
PRINT ON DEMAND Book; New; Publication Year 2017; Not Signed; Fast Shipping from the UK.
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors
ISBN: 9783668642591 bzw. 3668642591, in Deutsch, GRIN Verlag, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors
ISBN: 9783668642591 bzw. 3668642591, in Deutsch, GRIN Verlag, neu, E-Book, elektronischer Download.
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors
ISBN: 9783668642591 bzw. 3668642591, vermutlich in Englisch, neu, E-Book, elektronischer Download.
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors (2018)
ISBN: 9783668642607 bzw. 3668642605, in Englisch, 40 Seiten, Grin Verlag, Taschenbuch, neu.
Von Händler/Antiquariat, LitoA Buch- und Medienhandel.
Taschenbuch, Label: Grin Verlag, Grin Verlag, Produktgruppe: Book, Publiziert: 2018-02-26, Studio: Grin Verlag.