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

Fayed, Salema and Youssef, Sherin and El-Helw, Amr and Patwary, Mohammad and Moniri, Mansour (2015) Adaptive Compressive Sensing for Target Tracking within Wireless Visual Sensor Networks-based Surveillance applications. Multimedia Tools and Applications, 75 (11). pp. 6347-6371. ISSN 1380-7501

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Abstract

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume captured images in a way for energy-efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target's next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets.
Results have shown that with adaptive block CS up to 31% measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.

Item Type: Article
Additional Information: The final publication is available at Springer via https://doi.org/10.1007/s11042-015-2575-8
Uncontrolled Keywords: Adaptive Compressive Sensing , Compressive sensing, LMS Surveillance applications, Target tracking, WVSN
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cloud Computing
UoA Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Ian Mcdonald
Date Deposited: 07 Mar 2017 11:25
Last Modified: 06 Dec 2017 13:43
URI: http://www.open-access.bcu.ac.uk/id/eprint/3989

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