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Persons Activity Recognition in RGBD using Deep Learning

Zhao, Rui (2016) Persons Activity Recognition in RGBD using Deep Learning. Master's. DLR-Interner Bericht. DLR-IB-RM-OP-2016-200, 35 S.

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Abstract

The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes. Despite advances in sensing, in particular related to 3D video, the methodologies to process the data are still subject to research. We demonstrate superior results by a system which combines recurrent neural Networks with convolutional neural networks in a voting approach. The GRU-based recurrent neural networks are particularly wellsuited to distinguish actions based on long-term Information from optical tracking data; the 3D-CNNs focus more on detailed, recent information from video data. The resulting Features are merged in an SVM which then classifies the movement. In this architecture, our method improves recognition rates of state-of-the-art methods by 14% on standard data sets.

Item URL in elib:https://elib.dlr.de/110271/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Persons Activity Recognition in RGBD using Deep Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhao, RuiRui.Zhao (at) dlr.deUNSPECIFIED
Date:2016
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:35
Status:Published
Keywords:Video sequences, applications, health Monitoring, assisted living, surveillance, smart home
Institution:Technische Universität München
Department:Department of Informatics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Schlögl, Birgit
Deposited On:10 Jan 2017 09:42
Last Modified:10 Jan 2017 09:42

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