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Model-based tracking of laboratory animals

机译:基于模型的实验动物跟踪

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We present a system for tracking laboratory animals during pharmacological experiments. As it is usually possible to ensure good contrast between the animals and the background, tracking of a single animal or several physically separated animals can be achieved by relatively simple algorithms. The main problem arises when we try to track several almost identical, uniformly coloured animals during their contacts. To deal with this problem we represent objects by parametrically deformable contour models. The model has been built by observing videos containing a single animal (a laboratory mouse). To reflect symmetry, the model is axial and contains the offsets of the contour segments from the axis of minimal inertia. The deformation is modeled as stretching and bending. The tracking is done in two steps. For the tracking of objects from frame to frame we use the rigidity assumption, i.e. in the first step the contour models which represent objects in the previous frame are translated into new positions. In the second step the object position, rotation and scale, as well as the deformation parameters, are fine-tuned to match the object boundaries. The interframe translation is estimated by minimizing the sum of squared differences (SDD) over the search window for all tracked contour points. The model fitting is based on maximizing the contour energy in terms of the underlying smoothed gradient image. The robustness of the tracking algorithm is improved by adding a supervision module, which detects tracking failures and reinitializes the contours that lose their targets. The system has been tested on real sequences with laboratory animals during pharmacological experiments and has been shown to be robust and efficient. Future extensions will include expert knowledge of biomedical and pharmacological experts. The major goal is to build a system that will provide a tool for objective evaluation of animal behaviour during experiments.
机译:我们提出了在药理实验过程中跟踪实验动物的系统。由于通常可以确保动物与背景之间形成良好的对比,因此可以通过相对简单的算法来跟踪单个动物或几只身体分离的动物。当我们尝试跟踪几只几乎相同,颜色统一的动物接触时,就会出现主要问题。为了解决这个问题,我们通过参数可变形的轮廓模型来表示对象。该模型是通过观察包含一只动物(实验室老鼠)的视频而构建的。为了反映对称性,模型为轴向模型,其中包含轮廓线段与最小惯性轴的偏移。变形被建模为拉伸和弯曲。跟踪分为两个步骤。为了从一帧到另一帧跟踪对象,我们使用刚度假设,即在第一步中,将表示前一帧中的对象的轮廓模型转换为新位置。在第二步中,对对象的位置,旋转和比例以及变形参数进行微调以匹配对象边界。通过最小化所有跟踪轮廓点在搜索窗口上的平方差之和(SDD)来估计帧间转换。模型拟合的基础是根据基础平滑梯度图像最大化轮廓能量。跟踪算法的健壮性通过添加一个监督模块来提高,该模块可以检测跟踪故障并重新初始化丢失其目标的轮廓。该系统已在药理实验过程中与实验动物一起在真实序列上进行了测试,并已证明是可靠且有效的。未来的扩展将包括生物医学和药理学专家的专业知识。主要目标是建立一个系统,为实验过程中动物行为的客观评估提供工具。

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