A novel method for robust markerless tracking of rodent paws in 3D

Studying animal locomotion improves our understanding of motor control and aids in the treatment of motor impairment. Mice are a premier model of human disease and are the model system of choice for much of basic neuroscience. Placement of the tips of appendages, here paws, is typically critical for locomotion. Tracking paws from a video is difficult, however, due to frequent occlusions and collisions. We propose a method and provide software to track the paws of rodents. We use a superpixel-based method to segment the paws, direct linear transform to perform 3D reconstruction, a 3D Kalman filter (KF) to solve the matching problem and label paws across frames, and spline fits through time to resolve common collisions. The automated method was compared to manual tracking. The method had an average of 2.54 mistakes requiring manual correction per 1000 frames with a maximum of 5.29 possible errors while these values were estimates of the expected errors. We present an algorithm and its implementation to track the paws of running rodents. This algorithm can be applied to different animals as long as the tips of the legs can be differentiated from the background and other parts of the body using color features. The presented algorithm provides a robust tool for future studies in multiple fields, where precise quantification of locomotor behavior from a high-speed video is required. We further present a graphical user interface (GUI) to track, visualize, and edit the tracking data.