Zelman Ido, Galun Meirav, Akselrod-Ballin Ayelet, Yekutieli Yoram, Hochner Binyamin, Flash Tamar
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.
J Neurosci Methods. 2009 Aug 30;182(1):97-109. doi: 10.1016/j.jneumeth.2009.05.022. Epub 2009 Jun 6.
Tracking animal movements in 3D space is an essential part of many biomechanical studies. The most popular technique for human motion capture uses markers placed on the skin which are tracked by a dedicated system. However, this technique may be inadequate for tracking animal movements, especially when it is impossible to attach markers to the animal's body either because of its size or shape or because of the environment in which the animal performs its movements. Attaching markers to an animal's body may also alter its behavior. Here we present a nearly automatic markerless motion capture system that overcomes these problems and successfully tracks octopus arm movements in 3D space. The system is based on three successive tracking and processing stages. The first stage uses a recently presented segmentation algorithm to detect the movement in a pair of video sequences recorded by two calibrated cameras. In the second stage, the results of the first stage are processed to produce 2D skeletal representations of the moving arm. Finally, the 2D skeletons are used to reconstruct the octopus arm movement as a sequence of 3D curves varying in time. Motion tracking, segmentation and reconstruction are especially difficult problems in the case of octopus arm movements because of the deformable, non-rigid structure of the octopus arm and the underwater environment in which it moves. Our successful results suggest that the motion-tracking system presented here may be used for tracking other elongated objects.
在三维空间中跟踪动物的运动是许多生物力学研究的重要组成部分。最流行的人体运动捕捉技术是使用放置在皮肤上的标记物,并由专门的系统进行跟踪。然而,这种技术可能不足以跟踪动物的运动,特别是当由于动物的大小或形状,或者由于动物运动所处的环境而无法在其身体上附着标记物时。在动物身体上附着标记物也可能会改变其行为。在此,我们展示了一种几乎自动的无标记运动捕捉系统,该系统克服了这些问题,并成功地在三维空间中跟踪章鱼手臂的运动。该系统基于三个连续的跟踪和处理阶段。第一阶段使用最近提出的分割算法来检测由两个校准相机记录的一对视频序列中的运动。在第二阶段,对第一阶段的结果进行处理,以生成运动手臂的二维骨骼表示。最后,二维骨骼用于将章鱼手臂的运动重建为随时间变化的三维曲线序列。由于章鱼手臂的可变形、非刚性结构以及它在其中移动的水下环境,在章鱼手臂运动的情况下,运动跟踪、分割和重建是特别困难的问题。我们的成功结果表明,这里展示的运动跟踪系统可用于跟踪其他细长物体。