In recent years, researchers and laboratory support companies have recognized the utility of automated profiling of laboratory mouse activity and behavior in the home-cage. Video-based systems have emerged as a viable solution for non-invasive mouse monitoring. Wider use of vision systems for ethology studies requires the development of scalable hardware seamlessly integrated with vivarium ventilated racks. Compact hardware combined with automated video analysis would greatly impact animal science and animal-based research. Automated vision systems, free of bias and intensive labor, can accurately assess rodent activity (e.g., well-being) and behavior 24-7 during research studies within primary home-cages. Scalable compact hardware designs impose constraints, such as use of fisheye lenses, placing greater burden (e.g., distorted image) on downstream video analysis algorithms. We present novel methods for analysis of video acquired through such specialized hardware. Our algorithms estimate the 3D pose of mouse from monocular images. We present a thorough examination of the algorithm training parameters’ influence on system accuracy. Overall, the methods presented offer novel approaches for accurate activity and behavior estimation practical for large-scale use of vision systems in animal facilities.