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Obtaining three-dimensional trajectory of multiple fish in water tank via video tracking

机译：通过视频跟踪获得水箱中多条鱼的三维轨迹

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Accurately and reliably obtaining the three-dimensional motion data of individuals in fish schools is not only valuable for fish behaviour analysis and hydrodynamics studies but also be helpful in areas such as bio-inspired robot design. Video tracking is the most effective gateway to obtain the quantitative motion data of continuously moving objects. In this paper we propose a method for obtaining the quantitative three-dimensional trajectory of individuals in fish schools. The proposed method works on videos captured by multiple synchronized cameras with the help of a suggested three-camera imaging system. The proposed method follows the master-slave paradigm in which it tracks fish in the master view with the help of the convolutional neural network in the first, and then associates each certain fish in the master view with detections in the slave view by formulating the cross-view data association as the moment-wise linear assignment problems. Experiments have conducted on public datasets to completely evaluate the performance. The proposed method outperforms other state-of-the-art methods.
机译：准确可靠地获取鱼群中个体的三维运动数据，不仅对鱼的行为分析和流体动力学研究有帮助，而且在诸如生物灵感的机器人设计等领域也很有帮助。视频跟踪是获取连续运动物体定量运动数据的最有效途径。在本文中，我们提出了一种获取鱼类学校中个体的定量三维轨迹的方法。所提出的方法在建议的三摄像机成像系统的帮助下，可以处理由多个同步摄像机捕获的视频。所提出的方法遵循主-从范式，该方法首先在卷积神经网络的帮助下在主视图中跟踪鱼，然后通过制定叉，将主视图中的每个特定鱼与从视图中的检测关联视图数据关联作为瞬时线性分配问题。已经对公共数据集进行了实验，以完全评估性能。所提出的方法优于其他最新方法。

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