农业工程学报2026，Vol.42Issue(11)：39-48,10.DOI:10.11975/j.issn.1002-6819.202509106
基于FS-SWAGCN的羊只围产期行为视频识别方法
Video-based recognition method for the perinatal behaviors of ewes using FS-SWAGCN
摘要
Abstract
Precise monitoring of perinatal behaviors in ewes is crucial to improve reproductive efficiency with the low risk of dystocia in an intelligent livestock system.However,the current recognition of perinatal behaviors from video data has remained a major challenge,due to the long temporal duration,subtle motion patterns,and high inter-class similarity among behavioral features.In this study,a robust and efficient multi-modal framework was developed to recognize skeleton behavior using an improved Two-Stream Adaptive Graph Convolutional Network(2s-AGCN).A graph deep learning model was designed to capture both spatial and temporal dependencies in complex ewe behaviors.(1)Weighted Adaptive Graph Convolution Layer(W-AGCL)was introduced to overcome the limited adaptability of fixed skeletal topology when representing subtle or dynamic motion.The connection strengths between skeletal nodes were dynamically adjusted to adaptively learn the most informative spatial relationships,according to the behavioral context.The adaptive weighting mechanism enhanced the model sensitivity to spatial structure variations and the robustness to noise and individual differences among ewes.(2)Spatial Temporal Enhanced Attention Module(STE)was developed for the even temporal distribution and the presence of micro-movements in perinatal behaviors.The spatiotemporal regions were selectively emphasized to assign the higher attention weights into frames and joints with discriminative information,thereby improving the network to capture subtle but behaviorally significant cues.Furthermore,a four-stream graph convolutional architecture was proposed for the multi-modal feature fusion.Simultaneously,four complementary modalities were processed,including the Joint Stream representing skeletal joint coordinates,the Bone Stream capturing limb connectivity and orientation,the Joint Motion Stream describing temporal displacement of joints,and the Bone Motion Stream modeling dynamic variations in bone vectors.Among them,feature learning and deep interaction were integrated to clarify the static posture configurations and dynamic motion.A video dataset was constructed for the ewe perinatal behavior,including annotated samples of typical behaviors,such as standing,lying,turning,nest-building,and lambing.Experiments were then conducted to verify the model.The results show that the improved 2s-AGCN model achieved a Top-1 classification accuracy of 86.21％,thus outperforming several skeleton action recognition models,including ST-GCN,ST-GCN++,PoseC3D,Shift-GCN,CTR-GCN,and the original 2s-AGCN.Specifically,the Top-1 accuracies were 7.81,8.41,7.95,7.26,7.11,and 6.89 percentage points,respectively.The better performancewasachievedinthecompactarchitecturewith5.70millionparameters,andtheaverageinferencelatencyperframewas15.5ms,fully supporting real-time monitoring in farm environments.The skeleton graph convolutional models were used to recognize the fine-grained animal behaviors in the perinatal period.The improved 2s-AGCN framework effectively balanced accuracy,efficiency,and real-time inference.Spatiotemporal dependencies were adaptively learned to integrate multi-modal skeletal information.The findings can provide a powerful tool for automatic behavior in smart sheep farming.Deep graph learning can be expected to monitor livestock in future applications,such as early lambing prediction,automatic reproduction,and precision welfare assessment in smart pastures.关键词
行为识别/姿态估计/2S-AGCN/羊只围产期Key words
behavior recognition/pose estimation/2s-AGCN/periparturient sheep分类
农业科技引用本文复制引用
孙思晗,孙小华,王超,何振学,袁万哲,雷白时,王福顺..基于FS-SWAGCN的羊只围产期行为视频识别方法[J].农业工程学报,2026,42(11):39-48,10.基金项目
河北省重点研发计划项目(22327403D) （22327403D）
河北省现代农业产业技术体系羊产业创新团队专项资金项目(HBCT2024250204) （HBCT2024250204）
