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The Visual Computer

, Volume 35, Issue 5, pp 721–738 | Cite as

Automatic identification and behavioral analysis of phlebotomine sand flies using trajectory features

  • Ahmed Nejmedine MachraouiEmail author
  • Mohamed Fethi DiouaniEmail author
  • Aymen Mouelhi
  • Kaouther Jaouadi
  • Jamila Ghrab
  • Hafedh Abdelmelek
  • Mounir Sayadi
Original Article
  • 54 Downloads

Abstract

The present paper reports an automated approach for the characterization and analysis of the behavioral of sand flies; the method used is based on Gaussian mixture model and Kalman filter for the detection and tracking of sand flies, and then the extraction of an optimized set of features from the trajectory of flight is performed for the classification process. So, we propose here two optimized sets of features; the first one is used to identify sand flies among other insects, and the second is employed for the characterization of the behavioral change in the sand flies in the presence of a repulsive odor. These features are tested on three different classifiers; artificial neural network, support vector machine and K-nearest neighbor (KNN), and the results show an important improvement in the classification accuracy and confirm the effectiveness of our approach; the accuracy rate of the proposed method reached 88.6% for the identification of sand flies and 93.4% for the detection of their behavior change. Instead of the excessive use of pesticides over wide areas, the presented investigation is a key pillar of the development of an ecological way for a statistical information gathering about sand flies in order to fight against disease carried by those insects especially leishmaniosis and pappataci fever.

Keywords

Sand fly Object tracking Features extraction Motion analysis Computer vision Ecology 

Notes

Acknowledgements

This work was supported by the European Tropsense Project, ref: 645 758, H2020-MSCA-2014 RISE Program.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ahmed Nejmedine Machraoui
    • 1
    Email author
  • Mohamed Fethi Diouani
    • 2
    Email author
  • Aymen Mouelhi
    • 1
  • Kaouther Jaouadi
    • 3
  • Jamila Ghrab
    • 4
  • Hafedh Abdelmelek
    • 5
  • Mounir Sayadi
    • 1
  1. 1.Laboratory of Signal Image and Energy MasteryENSIT, Université de TunisTunisTunisia
  2. 2.Laboratory of Epidemiology and Veterinary MicrobiologyInstitut Pasteur de TunisTunisTunisia
  3. 3.Laboratory of Transmission, Control and Immunobiology of Infections (LR11IPT02), Department of Medical EpidemiologyInstitut Pasteur de TunisTunisTunisia
  4. 4.Laboratory of Medical ParasitologyBiotechnology and Biomolecules, Institut Pasteur de TunisTunisTunisia
  5. 5.Laboratory of Integrative PhysiologyFaculté des Sciences de BizerteBizerteTunisia

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