摘要Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing＞8 000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learning-based methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and naive background subtraction method.Using MonkeyTrail to analyze Iong-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.