Long-term non-contact tracking of caged rodents

Automatic tracking of rodents' behaviors over time in their home cages is of great interest in psycho-physiological studies. The commercially-available animal monitoring systems use RGB videos or bio-potential signals to monitor behaviors of animals when exploring their surroundings. The based models of these devices starts from several thousands of dollars and the cost would increase if extra analysis features were added. In this study, we present a low-cost, non-contact animal tracking system which records depth data from the caged rodent to detect the animal's location and pose over time. An adaptive Gaussian Mixture Model (GMM) algorithm is employed to detect animal's center of mass and extract its movement trajectory over an extended period of time. The animal's pose is determined by applying Principle Component Analysis (PCA) on 3D depth data of the located animal. In conjunction with our previously-introduced respiratory detection algorithm, this system can be utilized as an automatic long-term and unobtrusive monitoring system for animal experiments. We validated the tracking accuracy of our system by monitoring two different caged voles. The voles' locations were correctly detected in 80% of times, while the poses were detected correctly in 100% of times confirmed by visually inspecting the color-coded depth videos.