MooTrack360 is a novel top-down fisheye dataset designed to support the development of robust multi-camera surveillance and monitoring systems in large-scale, real-world environments. While centered around continuous livestock monitoring of Holstein dairy cows, the dataset addresses general challenges in computer vision such as fisheye distortion, overlapping fields of view, variable lighting, and occlusions. It includes 102,747 annotated cow instances across 1,500 images, each labeled as \textit{"standing"} or \textit{"lying"}, along with a 1-hour annotated video sequence for tracking evaluation. In addition, several unannotated sample sequences are included to support development and qualitative analysis. The dataset enables detection and tracking under conditions ranging from daylight to infrared-assisted nighttime imaging, and facilitates both application-specific and generalized model evaluation. A detailed calibration pipeline based on the Double Sphere Camera model is provided to support distortion correction and precise spatial localization. An accompanying end-to-end training framework further addresses challenges such as illumination changes and occlusions. Benchmarks using state-of-the-art detection and tracking methods demonstrate the dataset’s potential to advance research in non-invasive, camera-based monitoring across domains.