Probabilistic activity recognition in navigation

Octavi Font; Guillem Frances; Anders Jonsson; Phil Bartie; William Mackaness

doi:10.1109/WPNC.2014.6843309

Probabilistic activity recognition in navigation

Octavi Font, Guillem Frances, Anders Jonsson, Phil Bartie, William Mackaness

School of Geosciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities as long as it is possible to estimate the conditional probabilities of potential observations, and easily scales to large numbers of activities. We test our approach empirically in an environment where observations are GPS signals of users moving around in a city.

Original language	English
Title of host publication	2014 11th Workshop on Positioning, Navigation and Communication, WPNC, Dresdon
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Print)	9781479946716
DOIs	https://doi.org/10.1109/WPNC.2014.6843309
Publication status	Published - 11 Mar 2014
Event	11th IEEE Workshop on Positioning, Navigation and Communication, WPNC 2014 - Dresden, Germany Duration: 12 Mar 2014 → 13 Mar 2014

Conference

Conference	11th IEEE Workshop on Positioning, Navigation and Communication, WPNC 2014
Country/Territory	Germany
City	Dresden
Period	12/03/14 → 13/03/14

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10.1109/WPNC.2014.6843309

Cite this

@inproceedings{f77fd274db8f4fb09ac0f4f3921ac0c7,

title = "Probabilistic activity recognition in navigation",

abstract = "In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities as long as it is possible to estimate the conditional probabilities of potential observations, and easily scales to large numbers of activities. We test our approach empirically in an environment where observations are GPS signals of users moving around in a city.",

author = "Octavi Font and Guillem Frances and Anders Jonsson and Phil Bartie and William Mackaness",

year = "2014",

month = mar,

day = "11",

doi = "10.1109/WPNC.2014.6843309",

language = "English",

isbn = "9781479946716",

booktitle = "2014 11th Workshop on Positioning, Navigation and Communication, WPNC, Dresdon",

publisher = "Institute of Electrical and Electronics Engineers",

note = "11th IEEE Workshop on Positioning, Navigation and Communication, WPNC 2014 ; Conference date: 12-03-2014 Through 13-03-2014",

}

Font, O, Frances, G, Jonsson, A, Bartie, P & Mackaness, W 2014, Probabilistic activity recognition in navigation. in 2014 11th Workshop on Positioning, Navigation and Communication, WPNC, Dresdon., 6843309, Institute of Electrical and Electronics Engineers, 11th IEEE Workshop on Positioning, Navigation and Communication, WPNC 2014, Dresden, Germany, 12/03/14. https://doi.org/10.1109/WPNC.2014.6843309

TY - GEN

T1 - Probabilistic activity recognition in navigation

AU - Font, Octavi

AU - Frances, Guillem

AU - Jonsson, Anders

AU - Bartie, Phil

AU - Mackaness, William

PY - 2014/3/11

Y1 - 2014/3/11

N2 - In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities as long as it is possible to estimate the conditional probabilities of potential observations, and easily scales to large numbers of activities. We test our approach empirically in an environment where observations are GPS signals of users moving around in a city.

AB - In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities as long as it is possible to estimate the conditional probabilities of potential observations, and easily scales to large numbers of activities. We test our approach empirically in an environment where observations are GPS signals of users moving around in a city.

UR - http://www.scopus.com/inward/record.url?scp=84904568900&partnerID=8YFLogxK

U2 - 10.1109/WPNC.2014.6843309

DO - 10.1109/WPNC.2014.6843309

M3 - Conference contribution

AN - SCOPUS:84904568900

SN - 9781479946716

BT - 2014 11th Workshop on Positioning, Navigation and Communication, WPNC, Dresdon

PB - Institute of Electrical and Electronics Engineers

T2 - 11th IEEE Workshop on Positioning, Navigation and Communication, WPNC 2014

Y2 - 12 March 2014 through 13 March 2014

ER -

Probabilistic activity recognition in navigation

Abstract / Description of output

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