An Intelligent real-time occupancy monitoring system using single overhead camera

Jawad Ahmad; Hadi Larijani; Rohinton Emmanuel; Mike Mannion; Abbas Javed

doi:10.1007/978-3-030-01057-7_71

An Intelligent real-time occupancy monitoring system using single overhead camera

Jawad Ahmad, Hadi Larijani^*, Rohinton Emmanuel, Mike Mannion, Abbas Javed

^*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

Real-time occupancy monitoring information is an important component in building energy management and security. Advances in technology enables us to develop vision-based systems. These systems have gained popularity among different scientific research communities due to their high accuracy. Based on real-time video from a single camera, people occupancy rates in buildings can be correctly estimated using neural network models. This paper proposes an intelligent real-time bidirectional system, using Random Neural Network (RNN) predictions. An overhead camera was used to capture RGB images and the number of people crossing a virtual line was counted using the proposed counting technique. The proposed algorithm extracts some important features such as occupant blob areas, major axis, minor axis, eccentricity, perimeters and area-perimeter ratio for total 1000 frames. Finally, a RNN model is trained with aforementioned features using a gradient decent algorithm. Real-time experimental results show the effectiveness of the proposed method, especially when occupants are in group and blob merge/split scenarios. Real-time testing revealed an accuracy between 100 and 93.38% for single and multiple occupants, respectively.

Original language	English
Title of host publication	Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2
Editors	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Publisher	Springer Nature
Pages	957-969
Number of pages	13
Volume	869
ISBN (Electronic)	9783030010577
ISBN (Print)	9783030010560
DOIs	https://doi.org/10.1007/978-3-030-01057-7_71
Publication status	Published - 2019
Event	Intelligent Systems Conference 2018 - London, United Kingdom Duration: 6 Sept 2018 → 7 Sept 2018 https://saiconference.com/Conferences/IntelliSys2018

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	869
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Intelligent Systems Conference 2018
Abbreviated title	IntelliSys 2018
Country/Territory	United Kingdom
City	London
Period	6/09/18 → 7/09/18
Internet address	https://saiconference.com/Conferences/IntelliSys2018

Keywords

occupancy
video camera
video processing
neural network
image features
blob areas
Kalman filter

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Documents and Links

10.1007/978-3-030-01057-7_71

Cite this

Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., & Javed, A. (2019). An Intelligent real-time occupancy monitoring system using single overhead camera. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2 (Vol. 869, pp. 957-969). (Advances in Intelligent Systems and Computing; Vol. 869). Springer Nature. https://doi.org/10.1007/978-3-030-01057-7_71

Ahmad, Jawad ; Larijani, Hadi ; Emmanuel, Rohinton et al. / An Intelligent real-time occupancy monitoring system using single overhead camera. Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Vol. 869 Springer Nature, 2019. pp. 957-969 (Advances in Intelligent Systems and Computing).

@inproceedings{a6882416ef8842c3ad746b08773c417c,

title = "An Intelligent real-time occupancy monitoring system using single overhead camera",

abstract = "Real-time occupancy monitoring information is an important component in building energy management and security. Advances in technology enables us to develop vision-based systems. These systems have gained popularity among different scientific research communities due to their high accuracy. Based on real-time video from a single camera, people occupancy rates in buildings can be correctly estimated using neural network models. This paper proposes an intelligent real-time bidirectional system, using Random Neural Network (RNN) predictions. An overhead camera was used to capture RGB images and the number of people crossing a virtual line was counted using the proposed counting technique. The proposed algorithm extracts some important features such as occupant blob areas, major axis, minor axis, eccentricity, perimeters and area-perimeter ratio for total 1000 frames. Finally, a RNN model is trained with aforementioned features using a gradient decent algorithm. Real-time experimental results show the effectiveness of the proposed method, especially when occupants are in group and blob merge/split scenarios. Real-time testing revealed an accuracy between 100 and 93.38% for single and multiple occupants, respectively.",

keywords = "occupancy, video camera, video processing, neural network, image features, blob areas, Kalman filter",

author = "Jawad Ahmad and Hadi Larijani and Rohinton Emmanuel and Mike Mannion and Abbas Javed",

note = "Acceptance and AAM requested. ST 11/01/21. YYYY only found on publisher website for final pub date. ET 2/7/21 Crossref only has YYYY - CR (03/06/21); Intelligent Systems Conference 2018, IntelliSys 2018 ; Conference date: 06-09-2018 Through 07-09-2018",

year = "2019",

doi = "10.1007/978-3-030-01057-7_71",

language = "English",

isbn = "9783030010560",

volume = "869",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Nature",

pages = "957--969",

editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",

booktitle = "Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2",

address = "United States",

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}

Ahmad, J, Larijani, H , Emmanuel, R , Mannion, M & Javed, A 2019, An Intelligent real-time occupancy monitoring system using single overhead camera. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2. vol. 869, Advances in Intelligent Systems and Computing, vol. 869, Springer Nature, pp. 957-969, Intelligent Systems Conference 2018, London, United Kingdom, 6/09/18. https://doi.org/10.1007/978-3-030-01057-7_71

An Intelligent real-time occupancy monitoring system using single overhead camera. / Ahmad, Jawad; Larijani, Hadi ; Emmanuel, Rohinton et al.
Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Vol. 869 Springer Nature, 2019. p. 957-969 (Advances in Intelligent Systems and Computing; Vol. 869).

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

TY - GEN

T1 - An Intelligent real-time occupancy monitoring system using single overhead camera

AU - Ahmad, Jawad

AU - Larijani, Hadi

AU - Emmanuel, Rohinton

AU - Mannion, Mike

AU - Javed, Abbas

N1 - Acceptance and AAM requested. ST 11/01/21. YYYY only found on publisher website for final pub date. ET 2/7/21 Crossref only has YYYY - CR (03/06/21)

PY - 2019

Y1 - 2019

N2 - Real-time occupancy monitoring information is an important component in building energy management and security. Advances in technology enables us to develop vision-based systems. These systems have gained popularity among different scientific research communities due to their high accuracy. Based on real-time video from a single camera, people occupancy rates in buildings can be correctly estimated using neural network models. This paper proposes an intelligent real-time bidirectional system, using Random Neural Network (RNN) predictions. An overhead camera was used to capture RGB images and the number of people crossing a virtual line was counted using the proposed counting technique. The proposed algorithm extracts some important features such as occupant blob areas, major axis, minor axis, eccentricity, perimeters and area-perimeter ratio for total 1000 frames. Finally, a RNN model is trained with aforementioned features using a gradient decent algorithm. Real-time experimental results show the effectiveness of the proposed method, especially when occupants are in group and blob merge/split scenarios. Real-time testing revealed an accuracy between 100 and 93.38% for single and multiple occupants, respectively.

AB - Real-time occupancy monitoring information is an important component in building energy management and security. Advances in technology enables us to develop vision-based systems. These systems have gained popularity among different scientific research communities due to their high accuracy. Based on real-time video from a single camera, people occupancy rates in buildings can be correctly estimated using neural network models. This paper proposes an intelligent real-time bidirectional system, using Random Neural Network (RNN) predictions. An overhead camera was used to capture RGB images and the number of people crossing a virtual line was counted using the proposed counting technique. The proposed algorithm extracts some important features such as occupant blob areas, major axis, minor axis, eccentricity, perimeters and area-perimeter ratio for total 1000 frames. Finally, a RNN model is trained with aforementioned features using a gradient decent algorithm. Real-time experimental results show the effectiveness of the proposed method, especially when occupants are in group and blob merge/split scenarios. Real-time testing revealed an accuracy between 100 and 93.38% for single and multiple occupants, respectively.

KW - occupancy

KW - video camera

KW - video processing

KW - neural network

KW - image features

KW - blob areas

KW - Kalman filter

U2 - 10.1007/978-3-030-01057-7_71

DO - 10.1007/978-3-030-01057-7_71

M3 - Conference contribution

SN - 9783030010560

VL - 869

T3 - Advances in Intelligent Systems and Computing

SP - 957

EP - 969

BT - Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2

A2 - Arai, Kohei

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

PB - Springer Nature

T2 - Intelligent Systems Conference 2018

Y2 - 6 September 2018 through 7 September 2018

ER -

Ahmad J, Larijani H , Emmanuel R , Mannion M, Javed A. An Intelligent real-time occupancy monitoring system using single overhead camera. In Arai K, Kapoor S, Bhatia R, editors, Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2. Vol. 869. Springer Nature. 2019. p. 957-969. (Advances in Intelligent Systems and Computing). Epub 2018 Nov 8. doi: 10.1007/978-3-030-01057-7_71

An Intelligent real-time occupancy monitoring system using single overhead camera

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Documents and Links

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Cite this