Activity recognition from video data using spatial and temporal features

Mohamad Al-Wattar; Rinat Khusainov; Djamel Azzi; John Chiverton

doi:10.1109/IE.2016.57

Activity recognition from video data using spatial and temporal features

Mohamad Al-Wattar^*, Rinat Khusainov, Djamel Azzi, John Chiverton

^*Corresponding author for this work

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

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Abstract

A method to monitor elderly people in an indoor environment using conventional cameras is presented. The method can be used to identify people's activities and initiate suitable actions as needed. The originality of our approach is in combining spatial and temporal contexts with the position and orientation for the detected person. Preliminary evaluation, based only on the first two features (spatial and temporal), achieved the accuracy over 60% in a realistic residential environment. Although the results are based on using only two out of the four proposed input features, they already demonstrate a promising improvement over using a single feature in isolation.

Original language	English
Title of host publication	2016 12th International Conference on Intelligent Environments
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	250-253
Number of pages	4
ISBN (Electronic)	978-1-5090-4056-8
ISBN (Print)	978-1-5090-4057-5
DOIs	https://doi.org/10.1109/IE.2016.57
Publication status	Published - 27 Oct 2016
Event	12th International Conference on Intelligent Environments - London, United Kingdom Duration: 14 Sept 2016 → 16 Sept 2016

Publication series

Name
ISSN (Electronic)	2472-7571

Conference

Conference	12th International Conference on Intelligent Environments
Abbreviated title	IE 2016
Country/Territory	United Kingdom
City	London
Period	14/09/16 → 16/09/16

Keywords

Assisted living
Activity recognition
Context fusion
Temporal and spatial contexts

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IE.2016.57Licence: Unspecified

Activity Recognition from Video Data using Spatial and Temporal Features
© © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 320 KBLicence: Unspecified

Cite this

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title = "Activity recognition from video data using spatial and temporal features",

abstract = "A method to monitor elderly people in an indoor environment using conventional cameras is presented. The method can be used to identify people's activities and initiate suitable actions as needed. The originality of our approach is in combining spatial and temporal contexts with the position and orientation for the detected person. Preliminary evaluation, based only on the first two features (spatial and temporal), achieved the accuracy over 60% in a realistic residential environment. Although the results are based on using only two out of the four proposed input features, they already demonstrate a promising improvement over using a single feature in isolation.",

keywords = "Assisted living, Activity recognition, Context fusion, Temporal and spatial contexts",

author = "Mohamad Al-Wattar and Rinat Khusainov and Djamel Azzi and John Chiverton",

year = "2016",

month = oct,

day = "27",

doi = "10.1109/IE.2016.57",

language = "English",

isbn = "978-1-5090-4057-5",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "250--253",

booktitle = "2016 12th International Conference on Intelligent Environments",

address = "United States",

note = "12th International Conference on Intelligent Environments, IE 2016 ; Conference date: 14-09-2016 Through 16-09-2016",

}

Al-Wattar, M, Khusainov, R , Azzi, D & Chiverton, J 2016, Activity recognition from video data using spatial and temporal features. in 2016 12th International Conference on Intelligent Environments. Institute of Electrical and Electronics Engineers Inc., pp. 250-253, 12th International Conference on Intelligent Environments, London, United Kingdom, 14/09/16. https://doi.org/10.1109/IE.2016.57

TY - GEN

T1 - Activity recognition from video data using spatial and temporal features

AU - Al-Wattar, Mohamad

AU - Khusainov, Rinat

AU - Azzi, Djamel

AU - Chiverton, John

PY - 2016/10/27

Y1 - 2016/10/27

N2 - A method to monitor elderly people in an indoor environment using conventional cameras is presented. The method can be used to identify people's activities and initiate suitable actions as needed. The originality of our approach is in combining spatial and temporal contexts with the position and orientation for the detected person. Preliminary evaluation, based only on the first two features (spatial and temporal), achieved the accuracy over 60% in a realistic residential environment. Although the results are based on using only two out of the four proposed input features, they already demonstrate a promising improvement over using a single feature in isolation.

AB - A method to monitor elderly people in an indoor environment using conventional cameras is presented. The method can be used to identify people's activities and initiate suitable actions as needed. The originality of our approach is in combining spatial and temporal contexts with the position and orientation for the detected person. Preliminary evaluation, based only on the first two features (spatial and temporal), achieved the accuracy over 60% in a realistic residential environment. Although the results are based on using only two out of the four proposed input features, they already demonstrate a promising improvement over using a single feature in isolation.

KW - Assisted living

KW - Activity recognition

KW - Context fusion

KW - Temporal and spatial contexts

U2 - 10.1109/IE.2016.57

DO - 10.1109/IE.2016.57

M3 - Conference contribution

AN - SCOPUS:84998723880

SN - 978-1-5090-4057-5

SP - 250

EP - 253

BT - 2016 12th International Conference on Intelligent Environments

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th International Conference on Intelligent Environments

Y2 - 14 September 2016 through 16 September 2016

ER -

Activity recognition from video data using spatial and temporal features

Abstract

Publication series

Conference

Keywords

UN SDGs

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