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Human localization at home using kinect

Authors:

Tanushyam Chattopadhyay and

Sangheeta RoyAuthors Info & Claims

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

Pages 821 - 828

https://doi.org/10.1145/2494091.2497323

Published: 08 September 2013 Publication History

Abstract

In this paper authors have presented a method to localize and detect human being from Kinect captured sequence of images. The proposed method takes a sequence of gray (G) scale image and the corresponding depth (D) image as input. The gray scale image and the depth information are captured using two different sensors within the same device, Kinect and the processing are executed in the processor attached with Kinect. The proposed method localizes the human by using their motion along x, y direction and then considers all pixels connected with those pixels and over a 3D plane to accomplish the segmentation with an accuracy of 77%. Experimental results demonstrate that our method is robust against existing method for human localization.

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Cited By

Ahvar EDaneshgar-Moghaddam NOrtiz ALee GCrespi N(2016)On analyzing user location discovery methods in smart homesJournal of Network and Computer Applications10.1016/j.jnca.2016.09.01276:C(75-86)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.09.012
Oliver MMontero FFernández-Caballero AGonzález PMolina J(2015)RGB-D assistive technologies for acquired brain injuryExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1209632:3(370-380)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1111/exsy.12096
Roy SChattopadhyay T(2014)View-Invariant Human Detection from RGB-D Data of Kinect Using Continuous Hidden Markov ModelHuman-Computer Interaction. Advanced Interaction Modalities and Techniques10.1007/978-3-319-07230-2_32(325-336)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07230-2_32

Index Terms

Human localization at home using kinect
1. Applied computing
  1. Computers in other domains
2. Computer systems organization
  1. Real-time systems

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Estimation of Kinect depth confidence through self-training

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Published In

cover image ACM Conferences

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

1608 pages

ISBN:9781450322157

DOI:10.1145/2494091

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 08 September 2013

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UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

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UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

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Cited By

Ahvar EDaneshgar-Moghaddam NOrtiz ALee GCrespi N(2016)On analyzing user location discovery methods in smart homesJournal of Network and Computer Applications10.1016/j.jnca.2016.09.01276:C(75-86)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.09.012
Oliver MMontero FFernández-Caballero AGonzález PMolina J(2015)RGB-D assistive technologies for acquired brain injuryExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1209632:3(370-380)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1111/exsy.12096
Roy SChattopadhyay T(2014)View-Invariant Human Detection from RGB-D Data of Kinect Using Continuous Hidden Markov ModelHuman-Computer Interaction. Advanced Interaction Modalities and Techniques10.1007/978-3-319-07230-2_32(325-336)Online publication date: 2014
https://doi.org/10.1007/978-3-319-07230-2_32

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