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

3D Based Landmark Tracker Using Superpixels Based Segmentation for Neuroscience and Biomechanics Studies