Staffordshire University logo
STORE - Staffordshire Online Repository

Compressive Sensing for Target Detection and Tracking within Wireless Visual Sensor Networks-based Surveillance applications

Fayed, Salema Fathy (2016) Compressive Sensing for Target Detection and Tracking within Wireless Visual Sensor Networks-based Surveillance applications. Doctoral thesis, Staffordshire University.

Fayed_PhD thesis.pdf

Download (1MB) | Preview

Abstract or description

Wireless Visual Sensor Networks (WVSNs) have gained signi�cant importance
in the last few years and have emerged in several distinctive applications. The
main aim of this research is to investigate the use of adaptive Compressive Sens-
ing (CS) for e�cient target detection and tracking in WVSN-based surveillance
applications. CS is expected to overcome the WVSN resource constraints such
as memory limitation, communication bandwidth and battery constraints. In ad-
dition, adaptive CS dynamically chooses variable compression rates according to
di�erent data sets to represent captured images in an e�cient way hence saving
energy and memory space. In this work, a literature review on compressive sens-
ing, target detection and tracking for WVSN is carried out to investigate existing
techniques. Only single view target tracking is considered to keep minimum num-
ber of visual sensor nodes in a wake-up state to optimize the use of nodes and save
battery life which is limited in WVSNs. To reduce the size of captured images
an adaptive block CS technique is proposed and implemented to compress the
high volume data images before being transmitted through the wireless channel.
The proposed technique divides the image to blocks and adaptively chooses the
compression rate for relative blocks containing the target according to the sparsity
nature of images. At the receiver side, the compressed image is then reconstructed
and target detection and tracking are performed to investigate the e�ect of CS on
the tracking performance. Least mean square adaptive �lter is used to predicts
target's next location, an iterative quantized clipped LMS technique is proposed
and compared with other variants of LMS and results have shown that it achieved
lower error rates than other variants of lMS. The tracking is performed in both in-
door and outdoor environments for single/multi targets. Results have shown that
with adaptive block compressive sensing (CS) up to 31% measurements of data are
required to be transmitted for less sparse images and 15% for more sparse, while
preserving the 33dB image quality and the required detection and tracking perfor-
mance. Adaptive CS resulted in 82% energy saving as compared to transmitting
the required image with no CS.

Item Type: Thesis (Doctoral)
Faculty: Previous Faculty of Computing, Engineering and Sciences > Engineering
Depositing User: Jeffrey HENSON
Date Deposited: 12 Sep 2016 15:28
Last Modified: 06 Mar 2018 15:44

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000