Automated Tracking of Drosophila Specimens
The fruit fly Drosophila Melanogaster has become a model organism in the study of neurobiology and behavior patterns. The analysis of the way the fly moves and its behavior is of great scientific interest for research on aspects such as drug tolerance, aggression or ageing in humans. In this article...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/8/19369 |
| _version_ | 1798026950790348800 |
|---|---|
| author | Rubén Chao Germán Macía-Vázquez Eduardo Zalama Jaime Gómez-García-Bermejo José-Ramón Perán |
| author_facet | Rubén Chao Germán Macía-Vázquez Eduardo Zalama Jaime Gómez-García-Bermejo José-Ramón Perán |
| author_sort | Rubén Chao |
| collection | DOAJ |
| description | The fruit fly Drosophila Melanogaster has become a model organism in the study of neurobiology and behavior patterns. The analysis of the way the fly moves and its behavior is of great scientific interest for research on aspects such as drug tolerance, aggression or ageing in humans. In this article, a procedure for detecting, identifying and tracking numerous specimens of Drosophila by means of computer vision-based sensing systems is presented. This procedure allows dynamic information about each specimen to be collected at each moment, and then for its behavior to be quantitatively characterized. The proposed algorithm operates in three main steps: a pre-processing step, a detection and segmentation step, and tracking shape. The pre-processing and segmentation steps allow some limits of the image acquisition system and some visual artifacts (such as shadows and reflections) to be dealt with. The improvements introduced in the tracking step allow the problems corresponding to identity loss and swaps, caused by the interaction between individual flies, to be solved efficiently. Thus, a robust method that compares favorably to other existing methods is obtained. |
| first_indexed | 2024-04-11T18:43:34Z |
| format | Article |
| id | doaj.art-eac3aa5b95e34383a33c107aa04a88c7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:43:34Z |
| publishDate | 2015-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eac3aa5b95e34383a33c107aa04a88c72022-12-22T04:08:54ZengMDPI AGSensors1424-82202015-08-01158193691939210.3390/s150819369s150819369Automated Tracking of Drosophila SpecimensRubén Chao0Germán Macía-Vázquez1Eduardo Zalama2Jaime Gómez-García-Bermejo3José-Ramón Perán4University of Valladolid, Paseo del Cauce 59. Valladolid 47011, SpainUniversity of Valladolid, Paseo del Cauce 59. Valladolid 47011, SpainUniversity of Valladolid, Instituto de las Tecnologías Avanzadas de la Producción, Paseo del Cauce 59. Valladolid 47011, SpainUniversity of Valladolid, Instituto de las Tecnologías Avanzadas de la Producción, Paseo del Cauce 59. Valladolid 47011, SpainFundación Cartif, Parque Tecnológico de Boecillo, Valladolid 47151, SpainThe fruit fly Drosophila Melanogaster has become a model organism in the study of neurobiology and behavior patterns. The analysis of the way the fly moves and its behavior is of great scientific interest for research on aspects such as drug tolerance, aggression or ageing in humans. In this article, a procedure for detecting, identifying and tracking numerous specimens of Drosophila by means of computer vision-based sensing systems is presented. This procedure allows dynamic information about each specimen to be collected at each moment, and then for its behavior to be quantitatively characterized. The proposed algorithm operates in three main steps: a pre-processing step, a detection and segmentation step, and tracking shape. The pre-processing and segmentation steps allow some limits of the image acquisition system and some visual artifacts (such as shadows and reflections) to be dealt with. The improvements introduced in the tracking step allow the problems corresponding to identity loss and swaps, caused by the interaction between individual flies, to be solved efficiently. Thus, a robust method that compares favorably to other existing methods is obtained.http://www.mdpi.com/1424-8220/15/8/19369moving object sensingcomputer visiontrackingprediction methods |
| spellingShingle | Rubén Chao Germán Macía-Vázquez Eduardo Zalama Jaime Gómez-García-Bermejo José-Ramón Perán Automated Tracking of Drosophila Specimens Sensors moving object sensing computer vision tracking prediction methods |
| title | Automated Tracking of Drosophila Specimens |
| title_full | Automated Tracking of Drosophila Specimens |
| title_fullStr | Automated Tracking of Drosophila Specimens |
| title_full_unstemmed | Automated Tracking of Drosophila Specimens |
| title_short | Automated Tracking of Drosophila Specimens |
| title_sort | automated tracking of drosophila specimens |
| topic | moving object sensing computer vision tracking prediction methods |
| url | http://www.mdpi.com/1424-8220/15/8/19369 |
| work_keys_str_mv | AT rubenchao automatedtrackingofdrosophilaspecimens AT germanmaciavazquez automatedtrackingofdrosophilaspecimens AT eduardozalama automatedtrackingofdrosophilaspecimens AT jaimegomezgarciabermejo automatedtrackingofdrosophilaspecimens AT joseramonperan automatedtrackingofdrosophilaspecimens |