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Sensors 2015, 15(8), 19369-19392; doi:10.3390/s150819369

Automated Tracking of Drosophila Specimens

1
University of Valladolid, Paseo del Cauce 59. Valladolid 47011, Spain
2
University of Valladolid, Instituto de las Tecnologías Avanzadas de la Producción, Paseo del Cauce 59. Valladolid 47011, Spain
3
Fundación Cartif, Parque Tecnológico de Boecillo, Valladolid 47151, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 7 April 2015 / Revised: 7 July 2015 / Accepted: 27 July 2015 / Published: 6 August 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1935 KB, uploaded 6 August 2015]   |  

Abstract

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. View Full-Text
Keywords: moving object sensing; computer vision; tracking; prediction methods moving object sensing; computer vision; tracking; prediction methods
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chao, R.; Macía-Vázquez, G.; Zalama, E.; Gómez-García-Bermejo, J.; Perán, J.-R. Automated Tracking of Drosophila Specimens. Sensors 2015, 15, 19369-19392.

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