  Examining locomotion has improved our basic understanding of motor control
and aided in treating motor impairment. Mice and rats are premier models of
human disease and increasingly the model systems of choice for basic
neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics
of these running rodents. Manual tracking, especially for multiple markers,
becomes time-consuming and impossible for large sample sizes. Therefore, the
need for automatic segmentation of these markers has grown in recent years.
Here, we address this need by presenting a method to segment the markers using
the SLIC superpixel method. The 2D coordinates on the image plane are projected
to a 3D domain using direct linear transform (DLT) and a 3D Kalman filter has
been used to predict the position of markers based on the speed and position of
markers from the previous frames. Finally, a probabilistic function is used to
find the best match among superpixels. The method is evaluated for different
difficulties for tracking of the markers and it achieves 95% correct labeling
of markers.
