Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA.
J R Soc Interface. 2013 Jan 6;10(78):20120547. doi: 10.1098/rsif.2012.0547. Epub 2012 Oct 3.
The increasing interest in the investigation of social behaviours of a group of animals has heightened the need for developing tools that provide robust quantitative data. Drosophila melanogaster has emerged as an attractive model for behavioural analysis; however, there are still limited ways to monitor fly behaviour in a quantitative manner. To study social behaviour of a group of flies, acquiring the position of each individual over time is crucial. There are several studies that have tried to solve this problem and make this data acquisition automated. However, none of these studies has addressed the problem of keeping track of flies for a long period of time in three-dimensional space. Recently, we have developed an approach that enables us to detect and keep track of multiple flies in a three-dimensional arena for a long period of time, using multiple synchronized and calibrated cameras. After detecting flies in each view, correspondence between views is established using a novel approach we call the 'sequential Hungarian algorithm'. Subsequently, the three-dimensional positions of flies in space are reconstructed. We use the Hungarian algorithm and Kalman filter together for data association and tracking. We evaluated rigorously the system's performance for tracking and behaviour detection in multiple experiments, using from one to seven flies. Overall, this system presents a powerful new method for studying complex social interactions in a three-dimensional environment.
对一组动物的社会行为进行研究的兴趣日益浓厚，这就需要开发能够提供可靠定量数据的工具。黑腹果蝇已成为行为分析的一个有吸引力的模型；然而，仍然有有限的方法来以定量的方式监测果蝇的行为。为了研究一群果蝇的社会行为，随着时间的推移获取每个个体的位置是至关重要的。有几项研究试图解决这个问题，并使这种数据采集自动化。然而，这些研究都没有解决在三维空间中长时间跟踪果蝇的问题。最近，我们开发了一种方法，使用多个同步和校准的摄像机，能够在三维竞技场中长时间检测和跟踪多只果蝇。在检测到每个视图中的果蝇后，使用我们称之为“顺序匈牙利算法”的新方法建立视图之间的对应关系。随后，重建果蝇在空间中的三维位置。我们使用匈牙利算法和卡尔曼滤波器一起进行数据关联和跟踪。我们使用一到七只果蝇进行了多项实验，严格评估了该系统在跟踪和行为检测方面的性能。总的来说，这个系统为在三维环境中研究复杂的社会相互作用提供了一种强大的新方法。
