An automatic three dimensional markerless behavioral tracking system of free-moving mice

Understanding the behavior of mice relies on the precise tracking of its limbs' movement. In recent years, deep learning based animal pose estimation methods can track the user-defined body points of mice from single-view camera. However, mice perform the behavior in three dimensional (3D) space, and there is still no mature device to acquire them. Hence, this paper proposed an integrated 3D behavioral tracking system of free-moving mice. This system uses four multi-view cameras to capture the behavior of mice synchronously. The automatic calibration module simplifies the most time-consuming calibration step of multi-view cameras by merging Zhang's calibration method with a movable mechanical checkboard. With the calibration parameters, the 3D frame poses of mice can be reconstructed using the deep learning pose estimation trajectories of each camera. This 3D behavioral tracking system can avoid the occlusion problem caused by single-view camera, and calibrate multi-view camera efficiently, which can provide high-precision 3D poses data of free-moving mice with less human supervision.