MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages

Abstract

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 naïve background subtraction method. Using MonkeyTrail to analyze long-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.

Keywords: Background subtraction; Movement trajectory tracking; Occlusion; Video-based behavioral analyses; Virtual empty background.

Figure 1

Overall recording environment and camera setup

Figure 2

MonkeyTrail workflow

Figure 3

Influence of environmental changes on efficacy of background subtraction

Figure 4

Method to frequently update empty background

Figure 5

Background subtraction process with generated empty background

Figure 6

Representative tracking results for macaque during daytime and nighttime

Figure 7

Visualization of performance in generating bounding boxes by different methods

Figure 8

Tracking success rates with systematically varying overlap thresholds for different tracking methods

Figure 9

Daily total activity patterns of two monkeys (A and B) captured with MonkeyTrail

Figure 10

Spatial preference of macaques extracted by MonkeyTrail