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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 reinitialises 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:我们在药理学实验期间提出了一种跟踪实验室动物的系统。由于通常可以确保动物和背景之间的良好对比,因此通过相对简单的算法可以实现单个动物或几种物理分离的动物的跟踪。当我们试图在他们的触点期间尝试追踪几个几乎相同的,均匀的动物时,主要问题出现。要处理这个问题,我们通过参数可变形轮廓模型代表物体。该模型由观察包含单个动物(实验室鼠标)的视频构建。为了反射对称性,模型是轴向的,并且包含来自最小惯性轴的轮廓段的偏移。变形被建模为拉伸和弯曲。跟踪是以两步完成的。为了跟踪来自帧到帧的对象我们使用刚性假设,即在第一步中表示先前帧中对象的轮廓模型被转换为新位置。在第二步中,对象位置,旋转和缩放以及变形参数进行微调以匹配对象边界。通过为所有跟踪轮廓点最小化搜索窗口中的平方差(SDD)的总和来估计帧间帧转换。模型拟合基于基础的平滑梯度图像的轮廓能量最大化。通过添加监控模块来提高跟踪算法的稳健性,该模块检测跟踪故障并重新初始化失去目标的轮廓。该系统已经在药理学实验期间与实验室动物的真实序列进行了测试,并且已被证明是坚固且有效的未来延伸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,该系统将为实验期间提供动物行为的客观评估。

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