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Asset identification using image descriptors

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Abstract

Managing Information Technology (IT) assets in data centers is a time consuming and error prone process. IT personnel typically identify misplaced assets manually by cross checking and visually inspecting assets. An automated way of keeping track of assets using portable devices reduces human error and improves productivity. The proposed asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. Matching performance and response time of asset matching is evaluated using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for portable platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67 % when compared to the matching process using standard image feature descriptors.

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Acknowledgments

This project was funded by the NSF Industry/University Cooperative Research Center for Advanced Knowledge Enablement at Florida Atlantic University, NSF award No. 0934339.

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Author notes

  1. Reena Friedel

    Present address: The MathWorks, Inc., Natick, USA

Authors and Affiliations

  1. Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Reena Friedel, Oscar Figuerola, Hari Kalva & Borko Furht

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  1. Reena Friedel
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  2. Oscar Figuerola
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  3. Hari Kalva
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Corresponding author

Correspondence to Hari Kalva.

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Friedel, R., Figuerola, O., Kalva, H. et al. Asset identification using image descriptors. Multimed Tools Appl 73, 2201–2221 (2014). https://doi.org/10.1007/s11042-013-1688-1

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