• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Sep 30, 2019 News!Vol 9, No 6 has been published with online version. 12 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2017 Vol.7(5): 265-269 ISSN: 2010-3697
DOI: 10.7763/IJMO.2017.V7.595

Real-Time Object Detection and Recognition System Using OpenCV via SURF Algorithm in Emgu CV for Robotic Handling in Libraries

Corina Monica Pop, Gheorghe-Leonte Mogan, and Răzvan Gabriel Boboc
Abstract—This paper describes an experimental system that has been designed, implemented and tested for object recognition and tracking in still, respectively dynamic images – successive video frames captured in real time (live) with a web camera – based on Intel’s open source computer vision functions library, OpenCV (Open Source Computer Vision). We propose a real-time object recognition system in intelligent library environments. The system consists of two key modules: feature extraction and object recognition. Specific detectors such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Up Feature Robust) are efficient methods that provide high quality features, yet are too computational for use in real-time applications. This paper also proposes a low complexity, robust object recognition and tracking method using advanced real time feature matching. It combines Microsoft Visual Studio 2008 Express Edition C# with OpenCV Function library using SURF algorithm in Emgu CV to develop the software. The tests showed that the proposed system and method are more efficient and more robust than in most traditional applications.

Index Terms—Computer vision, object recognition, object tracking, OpenCV, SURF.

The authors are with the Faculty of Mechanical Engineering, Transilvania University of Brasov, Brasov, 500024 Romania (e-mail: popcorina@unitbv.ro, mogan@unitbv.ro, razvan.boboc@unitbv.ro).


Cite: Corina Monica Pop, Gheorghe-Leonte Mogan, and Răzvan Gabriel Boboc, "Real-Time Object Detection and Recognition System Using OpenCV via SURF Algorithm in Emgu CV for Robotic Handling in Libraries," International Journal of Modeling and Optimization vol. 7, no. 5, pp. 265-269, 2017.

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