A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

Article


Fayed, Salema, Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M. 2015. A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks. International Journal of Circuits, Systems, and Signal Processing. 9, pp. 134-144.
AuthorsFayed, Salema, Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M.
Abstract

The employ of Wireless Visual Sensor Networks (WVSNs)
has grown enormously in the last few years and have emerged in distinctive
applications. WVSNs-based Surveillance applications are one of the important
applications that requires high detection reliability and robust tracking,
while minimizing the usage of energy to maximize the lifetime of sensor
nodes as visual sensor nodes can be left for months without any human
interaction. The constraints of WVSNs such as resource constraints due to
limited battery power, memory space and communication bandwidth have
brought new WVSNs implementation challenges. Hence, the aim of this
paper is to investigate the impact of adaptive Compressive Sensing (CS) in
designing efficient target detection and tracking techniques, to reduce the
size of transmitted data without compromising the tracking performance as
well as space and energy constraints. In this paper, a new hybrid adaptive
compressive sensing scheme is introduced to dynamically achieve higher
compression rates, as different datasets have different sparsity nature that
affects the compression. Afterwards, a modified quantized clipped Least
Mean square (LMS) adaptive filter is proposed for the tracking model.
Experimental results showed that adaptive CS achieved high compression
rates reaching 70%, while preserving the detection and tracking accuracy
which is measured in terms of mean squared error, peak-signal-to-noise-ratio
and tracking trajectory.

JournalInternational Journal of Circuits, Systems, and Signal Processing
Journal citation9, pp. 134-144
ISSN1998-4464
Year2015
PublisherNorth Atlantic University Union
Web address (URL)http://www.naun.org/main/NAUN/circuitssystemssignal/2015/a382005-027.pdf
Publication dates
Print2015
Publication process dates
Deposited22 Aug 2017
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