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Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Title
Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors
Author
김기범
Keywords
cross entropy; depth sensors; Gaussian mixture model; maximum entropy Markov model
Issue Date
2020-07
Publisher
MDPI
Citation
ENTROPY, v. 22, no. 8, Article no. 817, 33pp
Abstract
Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features-i.e., spatio-temporal features, energy-based features, shape based angular and geometric features-and a motion-orthogonal histogram of oriented gradient (MO-HOG)
URI
http://eds.a.ebscohost.com/eds/detail/detail?vid=0&sid=5a8fda6c-80bc-4973-b77f-ca4e566311b1%40sdc-v-sessmgr03&bdata=Jmxhbmc9a28mc2l0ZT1lZHMtbGl2ZQ%3d%3d#AN=000564177800001&db=edswschttps://repository.hanyang.ac.kr/handle/20.500.11754/164787
ISSN
1099-4300
DOI
10.3390/e22080817
Appears in Collections:
ETC[S] > 연구정보
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