Probabilistic activity recognition in navigation

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

doi:10.1109/WPNC.2014.6843309

Probabilistic activity recognition in navigation

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

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

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.

Original language	English
Title of host publication	2014 11th Workshop on Positioning, Navigation and Communication, WPNC 2014
ISBN (Electronic)	978-1-4799-4671-6
DOIs	https://doi.org/10.1109/WPNC.2014.6843309
Publication status	Published - 2014

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10.1109/WPNC.2014.6843309

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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 Franc{\`e}s and Anders Jonsson and Phil Bartie and William Mackaness",

year = "2014",

doi = "10.1109/WPNC.2014.6843309",

language = "English",

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AU - Font, Octavi

AU - Francès, Guillem

AU - Jonsson, Anders

AU - Bartie, Phil

AU - Mackaness, William

PY - 2014

Y1 - 2014

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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.

U2 - 10.1109/WPNC.2014.6843309

DO - 10.1109/WPNC.2014.6843309

M3 - Conference contribution

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

ER -

Probabilistic activity recognition in navigation

Abstract

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