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A two-fly tracker that solves occlusions by dynamic programming: computational analysis of Drosophila courtship behaviour

机译:通过动态编程解决遮挡的两次跟踪追踪器:果蝇求爱行为的计算分析

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This paper introduces a two-fly tracker which focuses on an approach to model and to solve occlusions as an optimization problem. Automated tracking of genetic model organisms is gaining importance since geneticists and neuroscientists have biological tools to systematically study the connection between genes, neurons and behaviour by performing large-scale behavioural experiments. This paper is about a fly tracker that provides automated quantification for such functional behaviour studies on Drosophila courtship behaviour. It enables measurement and visualization of behavioural differences in genetically modified fly pairs. The developed system provides solutions for all major challenges that were identified: arena detection, segmentation, quality control, resolving occlusions, resolving heading and detection of behaviour events. Among all challenges especially resolving occlusions turned out to be of particular importance and huge effort was invested to resolve that particular problem. Our tests show that our system is capable to identify flies through an entire video with an accuracy of 99.97%. This result is achieved by combining different types of local methods and modeling the global identity assignment as an optimization problem.
机译:本文介绍了一次两次跟踪跟踪器,该跟踪器着重于建模和解决遮挡作为优化问题的方法。由于遗传学家和神经科学家拥有通过进行大规模行为实验系统地研究基因,神经元与行为之间联系的生物学工具,因此自动跟踪遗传模型生物变得越来越重要。本文是关于一种蝇追踪器,它可以对果蝇求爱行为的这种功能行为研究提供自动量化。它可以测量和可视化转基因蝇对中的行为差异。所开发的系统为已发现的所有主要挑战提供解决方案:舞台检测,分割,质量控制,解决遮挡,解决航向和行为事件检测。在所有挑战中,特别是解决遮挡特别重要,因此已投入巨大的努力来解决该特定问题。我们的测试表明,我们的系统能够识别整个视频中的苍蝇,准确率高达99.97%。通过组合不同类型的本地方法并将全局标识分配建模为优化问题来实现此结果。

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