YoloSortC3D: A YOLOv8, DeepSort, and C3D-Based Method for Sheep Rumination Behavior Recognition

The rumination behavior of sheep is closely linked to their overall health, making its accurate identification and quantification crucial. However, traditional manual observation methods by farm workers are both time-consuming and labor-intensive. Similarly, while wearable sensors can provide automated monitoring, they are expensive and may cause stress to the animals. This study proposes a deep learning and computer-vision-based approach to recognize and quantify sheep rumination behavior by calculating and recording rumination duration. This study presents a method that integrates the YOLOv8 object detection algorithm with the DeepSORT, multiobject tracking algorithm to monitor sheep head movements. Firstly, head images are identified and enclosed within ID-labeled bounding boxes, then cropped from video frames for further processing. The preprocessing steps include pixel padding, resizing, and center cropping. These processed images are subsequently stored for later analysis. Following this, a 3D convolutional neural network (3D-CNN) is employed to recognize rumination behavior, while the frame difference method is utilized to accurately calculate rumination duration. Lastly, to validate the effectiveness of the proposed approach, tests were conducted on multitarget sheep rumination videos, and the results were compared with manual observations. Experimental results demonstrated an average error of 4.403% during the day and 6.156% at night, confirming the feasibility and accuracy of this method for the automated detection and quantification of sheep rumination behavior.