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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Laboratory of Signal Image and Energy Mastery, ENSIT, Université de Tunis, Tunis, Tunisia

    Ahmed Nejmedine Machraoui, Aymen Mouelhi & Mounir Sayadi

  2. Laboratory of Epidemiology and Veterinary Microbiology, Institut Pasteur de Tunis, Tunis, Tunisia

    Mohamed Fethi Diouani

  3. Laboratory of Transmission, Control and Immunobiology of Infections (LR11IPT02), Department of Medical Epidemiology, Institut Pasteur de Tunis, Tunis, Tunisia

    Kaouther Jaouadi

  4. Laboratory of Medical Parasitology, Biotechnology and Biomolecules, Institut Pasteur de Tunis, Tunis, Tunisia

    Jamila Ghrab

  5. Laboratory of Integrative Physiology, Faculté des Sciences de Bizerte, Bizerte, Tunisia

    Hafedh Abdelmelek

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  1. Ahmed Nejmedine Machraoui
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  2. Mohamed Fethi Diouani
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  3. Aymen Mouelhi
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  7. Mounir Sayadi
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Correspondence to Ahmed Nejmedine Machraoui or Mohamed Fethi Diouani.

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Machraoui, A.N., Diouani, M.F., Mouelhi, A. et al. Automatic identification and behavioral analysis of phlebotomine sand flies using trajectory features. Vis Comput 35, 721–738 (2019). https://doi.org/10.1007/s00371-018-1506-x

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