Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation, ETFA 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781467379298 |
| Publication status | Published - 19 Oct 2015 |
| Event | 20th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2015 - Luxembourg, Luxembourg Duration: 8 Sept 2015 → 11 Sept 2015 |
Publication series
| Name | IEEE International Conference on Emerging Technologies and Factory Automation, ETFA |
|---|---|
| Volume | 2015-October |
| ISSN (Print) | 1946-0740 |
| ISSN (electronic) | 1946-0759 |
Abstract
In this work, we improve the capability of spatial event localization in ad-hoc sensor networks equipped with simple sensor devices, in the context of smart camera networks. In our former work, the Orthogonal Cut approach has been used for that purpose. It is applicable in many different object localization scenarios without cooperation of the object and without much knowledge about the environment. It estimates the spatial position of a detected event by dividing the surveillance space of a sensor network into smaller areas until a threshold criterion is met. However that approach is limited to localization of a single object. In this paper, we extend the aforementioned algorithm for localization of multiple events (objects), introducing a Grid-Based Orthogonal Cut algorithm. This improved algorithm is able to detect and differentiate multiple objects within the same area, based on the strength of the sensed signal. We evaluate the performance of the algorithm and compare our algorithm with the solutions based on the individual sensor positions.
Keywords
- Cameras, Delays, Estimation, Measurement errors, Sensor arrays, Surveillance
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
- Computer Science(all)
- Computer Science Applications
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Proceedings of 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation, ETFA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7301448 (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA; Vol. 2015-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Grid-Based Orthogonal Cut algorithm for value-based multiple events localization in sensor networks
AU - Fu, Desheng
AU - Becker, Matthias
AU - Schaust, Sven
AU - Szczerbicka, Helena
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/10/19
Y1 - 2015/10/19
N2 - In this work, we improve the capability of spatial event localization in ad-hoc sensor networks equipped with simple sensor devices, in the context of smart camera networks. In our former work, the Orthogonal Cut approach has been used for that purpose. It is applicable in many different object localization scenarios without cooperation of the object and without much knowledge about the environment. It estimates the spatial position of a detected event by dividing the surveillance space of a sensor network into smaller areas until a threshold criterion is met. However that approach is limited to localization of a single object. In this paper, we extend the aforementioned algorithm for localization of multiple events (objects), introducing a Grid-Based Orthogonal Cut algorithm. This improved algorithm is able to detect and differentiate multiple objects within the same area, based on the strength of the sensed signal. We evaluate the performance of the algorithm and compare our algorithm with the solutions based on the individual sensor positions.
AB - In this work, we improve the capability of spatial event localization in ad-hoc sensor networks equipped with simple sensor devices, in the context of smart camera networks. In our former work, the Orthogonal Cut approach has been used for that purpose. It is applicable in many different object localization scenarios without cooperation of the object and without much knowledge about the environment. It estimates the spatial position of a detected event by dividing the surveillance space of a sensor network into smaller areas until a threshold criterion is met. However that approach is limited to localization of a single object. In this paper, we extend the aforementioned algorithm for localization of multiple events (objects), introducing a Grid-Based Orthogonal Cut algorithm. This improved algorithm is able to detect and differentiate multiple objects within the same area, based on the strength of the sensed signal. We evaluate the performance of the algorithm and compare our algorithm with the solutions based on the individual sensor positions.
KW - Cameras
KW - Delays
KW - Estimation
KW - Measurement errors
KW - Sensor arrays
KW - Surveillance
UR - http://www.scopus.com/inward/record.url?scp=84952949357&partnerID=8YFLogxK
U2 - 10.1109/ETFA.2015.7301448
DO - 10.1109/ETFA.2015.7301448
M3 - Conference contribution
AN - SCOPUS:84952949357
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
BT - Proceedings of 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation, ETFA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2015
Y2 - 8 September 2015 through 11 September 2015
ER -