Enhancing Performance of Face Detection in Visual Sensor Networks with a Dynamic-based Approach

Enhancing Performance of Face Detection in Visual Sensor Networks with a Dynamic-based Approach A visual sensor network, as a special kind of wireless sensor network, is comprised of self-organizing sensor nodes, where some or all of them are equipped with a low-power camera. In most of the visual sensor network’s applications, human detection and recognition are one of the first challenges. Many different approaches that have been proposed to solve the object detection problem in visual sensor networks cause high transition cost in the network, and they are suitable for single object detection. Therefore, in this paper, we propose a new dynamic light-weight method to detect the faces of the objects in visual sensor networks. The proposed method employs a background subtraction technique, image reduction method, new low-complexity algorithm for extracting the bounding boxes of multiple objects and an innovative dynamic function that obtained from a probabilistic model for cutting each human face’s pixels. The proposed method is called Dynamic Light-weight Human Faces Detection (DLHFD). Our proposed method removes non-face pixels in camera nodes with a new energy-aware face detection method and sends only the faces’ information to the base station. The results from the implementation show that DLHFD method reduces the complexity of the image processing tasks on each camera node. Accordingly, the visual sensor network will last longer. Furthermore, the results prove that DLHFD method has very low processing and transition delay in comparison with other face detection methods. Hence, it is suitable for different monitoring and surveillance applications in visual sensor networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Enhancing Performance of Face Detection in Visual Sensor Networks with a Dynamic-based Approach

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4832-9
Publisher site
See Article on Publisher Site

Abstract

A visual sensor network, as a special kind of wireless sensor network, is comprised of self-organizing sensor nodes, where some or all of them are equipped with a low-power camera. In most of the visual sensor network’s applications, human detection and recognition are one of the first challenges. Many different approaches that have been proposed to solve the object detection problem in visual sensor networks cause high transition cost in the network, and they are suitable for single object detection. Therefore, in this paper, we propose a new dynamic light-weight method to detect the faces of the objects in visual sensor networks. The proposed method employs a background subtraction technique, image reduction method, new low-complexity algorithm for extracting the bounding boxes of multiple objects and an innovative dynamic function that obtained from a probabilistic model for cutting each human face’s pixels. The proposed method is called Dynamic Light-weight Human Faces Detection (DLHFD). Our proposed method removes non-face pixels in camera nodes with a new energy-aware face detection method and sends only the faces’ information to the base station. The results from the implementation show that DLHFD method reduces the complexity of the image processing tasks on each camera node. Accordingly, the visual sensor network will last longer. Furthermore, the results prove that DLHFD method has very low processing and transition delay in comparison with other face detection methods. Hence, it is suitable for different monitoring and surveillance applications in visual sensor networks.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Aug 12, 2017

References

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