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Analysis and Characterization of Embedded Vision Systems for Taxonomy Formulation
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0003-1923-3843
School of Engineering at the University of Edinburgh,UK.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-6484-9260
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
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2013 (English)In: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Nasser Kehtarnavaz, Matthias F. Carlsohn,, USA: SPIE - International Society for Optical Engineering, 2013, Art. no. 86560J- p.Conference paper, Published paper (Refereed)
Abstract [en]

The current trend in embedded vision systems is to propose bespoke solutions for specific problems as each application has different requirement and constraints. There is no widely used model or benchmark which aims to facilitate generic solutions in embedded vision systems. Providing such model is a challenging task due to the wide number of use cases, environmental factors, and available technologies. However, common characteristics can be identified to propose an abstract model. Indeed, the majority of vision applications focus on the detection, analysis and recognition of objects. These tasks can be reduced to vision functions which can be used to characterize the vision systems. In this paper, we present the results of a thorough analysis of a large number of different types of vision systems. This analysis led us to the development of a system’s taxonomy, in which a number of vision functions as well as their combination characterize embedded vision systems. To illustrate the use of this taxonomy, we have tested it against a real vision system that detects magnetic particles in a flowing liquid to predict and avoid critical machinery failure. The proposed taxonomy is evaluated by using a quantitative parameter which shows that it covers 95 percent of the investigated vision systems and its flow is ordered for 60 percent systems. This taxonomy will serve as a tool for classification and comparison of systems and will enable the researchers to propose generic and efficient solutions for same class of systems.

Place, publisher, year, edition, pages
USA: SPIE - International Society for Optical Engineering, 2013. Art. no. 86560J- p.
Series
Proceedings of SPIE, ISSN 0277-786X ; 8656
Keyword [en]
System taxonomy, Smart cameras, Embedded vision systems, Wireless vision sensor networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-16035DOI: 10.1117/12.2000584ISI: 000333051900018Scopus ID: 2-s2.0-84875855354Local ID: STCISBN: 978-0-8194-9429-0 (print)OAI: oai:DiVA.org:miun-16035DiVA: diva2:513132
Conference
Real-Time Image and Video Processing 2013; Burlingame, CA; United States; 6 February 2013 through 7 February 2013; Code 96385
Available from: 2013-02-05 Created: 2012-03-30 Last updated: 2016-10-20Bibliographically approved
In thesis
1. Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node
Open this publication in new window or tab >>Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Wireless Vision Sensor Networks (WVSNs) is an emerging field which has attracted a number of potential applications because of smaller per node cost, ease of deployment, scalability and low power stand alone solutions. WVSNs consist of a number of wireless Vision Sensor Nodes (VSNs). VSN has limited resources such as embedded processing platform, power supply, wireless radio and memory.  In the presence of these limited resources, a VSN is expected to perform complex vision tasks for a long duration of time without battery replacement/recharging. Currently, reduction of processing and communication energy consumptions have been major challenges for battery operated VSNs. Another challenge is to propose generic solutions for a VSN so as to make these solutions suitable for a number of applications.

To meet these challenges, this thesis focuses on energy efficient and programmable VSN architecture for machine vision systems which can classify objects based on binary data. In order to facilitate generic solutions, a taxonomy has been developed together with a complexity model which can be used for systems’ classification and comparison without the need for actual implementation. The proposed VSN architecture is based on tasks partitioning between a VSN and a server as well as tasks partitioning locally on the node between software and hardware platforms. In relation to tasks partitioning, the effect on processing, communication energy consumptions, design complexity and lifetime has been investigated.

The investigation shows that the strategy, in which front end tasks up to segmentation, accompanied by a bi-level coding, are implemented on Field Programmable Platform (FPGA) with small sleep power, offers a generalized low complexity and energy efficient VSN architecture. The implementation of data intensive front end tasks on hardware reconfigurable platform reduces processing energy. However, there is a scope for reducing communication energy, related to output data. This thesis also explores data reduction techniques including image coding, region of interest coding and change coding which reduces output data significantly.

For proof of concept, VSN architecture together with tasks partitioning, bi-level video coding, duty cycling and low complexity background subtraction technique has been implemented on real hardware and functionality has been verified for four applications including particle detection system, remote meter reading, bird detection and people counting. The results based on measured energy values shows that, depending on the application, the energy consumption can be reduced by a factor of approximately 1.5 up to 376 as compared to currently published VSNs. The lifetime based on measured energy values showed that for a sample period of 5 minutes, VSN can achieve 3.2 years lifetime with a battery of 37.44 kJ energy. In addition to this, proposed VSN offers generic architecture with smaller design complexity on hardware reconfigurable platform and offers easy adaptation for a number of applications as compared to published systems.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2013. 115 p.
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 167
Keyword
Wireless Vision Sensor Node, Smart camera, Wireless Vision Sensor Networks, Architecture, Video coding.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-20179 (URN)STC (Local ID)978-91-87557-12-5 (ISBN)STC (Archive number)STC (OAI)
Public defence
2013-10-22, M108, holmgatan 10,SE 85170, sundsvall, 10:03 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2013-11-11 Created: 2013-11-11 Last updated: 2016-10-20Bibliographically approved

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