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Titlebook: Human Behavior Understanding; Second International Albert Ali Salah,Bruno Lepri Conference proceedings 2011 Springer-Verlag GmbH Berlin Hei

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發(fā)表于 2025-3-21 18:49:41 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Human Behavior Understanding
副標(biāo)題Second International
編輯Albert Ali Salah,Bruno Lepri
視頻videohttp://file.papertrans.cn/429/428982/428982.mp4
概述Fast track conference proceedings.Unique visibility.State of the art research
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Human Behavior Understanding; Second International Albert Ali Salah,Bruno Lepri Conference proceedings 2011 Springer-Verlag GmbH Berlin Hei
描述This book constitutes the refereed proceedings of the Second International Workshop on Human Behavior Understanding, HBU 2011, held in Amsterdam, The Netherlands, in November 2011, in conjunction with AmI-11, the International Joint Conference on Ambient Intelligence. .The 13 revised full papers presented together with 2 keynote talks and one summarizing paper were carefully reviewed and selected from 32 submissions. The papers are organized in topical sections on analysis of human actions and activities, face and gesture analysis, persuasive technologies, and social interactions.
出版日期Conference proceedings 2011
關(guān)鍵詞ambient intelligence; energy saving support; human action recognition; mobile application; spatiotempora
版次1
doihttps://doi.org/10.1007/978-3-642-25446-8
isbn_softcover978-3-642-25445-1
isbn_ebook978-3-642-25446-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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Urban Computing and Smart Cities: Opportunities and Challenges in Modelling Large-Scale Aggregated Hlization, large amounts of data –digital footprints– can be sensed and analyzed to uncover patterns of human urban behavior and to augment the city experience of its citizens. In my talk, I will introduce the main concepts, opportunities and challenges in the emerging area of urban computing/smart c
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Sequential Deep Learning for Human Action Recognition scheme, based on the extension of . to 3D, automatically learns spatio-temporal features. A . is then trained to classify each sequence considering the temporal evolution of the learned features for each timestep. Experimental results on the KTH dataset show that the proposed approach outperforms e
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DTW Based Clustering to Improve Hand Gesture Recognitionth machine learning methods. In this work, we propose a dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) literature. A dataset of 1200 samples consisting of th
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Augmenting Social Interactions: Experiments in Socio-emotional Computings leads to the new, multidisciplinary area of socio-emotional computing, where computing technologies are applied to transform and enrich communication between people, either mediated or face-to-face. As a research field, socio-emotional computing serves a number of goals. First, it aims to inform t
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