Recognizing pawing behavior of prepartum doe using semantic segmentation and motion history image (MHI) features.

When a doe is approaching delivery, it often exhibits some specific behaviors such as eating less, sleeping less and pawing (scraping the floor with the hoof). Automatic recognition of these behaviors is valuable for farmers to determine whether does are nearing parturition or not. However, it is difficult for humans to recognize the behavior of pawing because movements are subtle and the foreleg is small. In this paper, we proposed a method to recognize the pawing behavior of prepartum does based on semantic segmentation, motion history image (MHI), and multi-scale histogram of oriented gradient (MS-HOG) features. Firstly, the foreleg region of the doe is segmented using YOLO-v5 and Unet. Next, MHI features are extracted from consecutive frames to represent the motion information of foreleg. Since MHI is insufficient to describe the local textures and shape information of the foreleg movements, we extracted MS-HOG based on the MHI features. A support vector machine (SVM) classifier was trained on the extracted features to determine whether or not a given frame contains pawing behavior. Furthermore, a word frequency vector was employed to characterize the proportion of pawing frames in a video episode with a length of one second to facilitate the recognition of pawing behavior. The proposed method was validated using both shorter video episodes and long videos. For the video episodes, our method achieved a classification accuracy of 93.14% and an average error rate of 9.72% in estimating the duration of the behavior. For the long videos, our method achieved a recall of 87.6% and precision of 77.8% when considering a temporal Intersection over Union (t I o U ≥ 0. 5). In summary, this study can be further combined with other characteristic behaviors to predict the impending delivery time of the does. • An automated method for identifying prepartum doe's pawing behavior. • Segment small target (foreleg) using a two-stage semantic segmentation method. • Reduce the computational overhead of feature extraction using multi-scale HOG. • Achieved 93.14% accuracy in pawing behavior recognition for video episodes. • Achieved 77.8% precision, 87.6% recall in long video considering tIOU ≥ 0.5. [ABSTRACT FROM AUTHOR]