智慧农业（中英文）2026，Vol.8Issue(4)：148-163,16.DOI:10.12133/j.smartag.SA202601012
基于改进AdaTAD的奶山羊爬跨行为时序动作定位方法
Temporal Action Localization of Mounting Behavior in Dairy Goats Based on Improved AdaTAD
摘要
Abstract
[Objective]Accurate temporal localization of mounting behavior in dairy goats is important for intelligent reproductive man-agement,as event frequency,onset time,and duration provide useful evidence for heat monitoring and mating decisions.Unlike sim-ple behavior recognition,temporal localization in untrimmed videos enables fine-grained,time-resolved records for practical farm use.However,real-world mounting behavior is usually brief and sporadic,with few informative frames in long video streams.Moreover,weak discrimination from similar non-target interactions,together with occlusion,viewpoint variation,and background motion,often degrades boundary-aware representation learning and leads to unstable start-end localization.To enhance localization accuracy and sta-bility while maintaining practical efficiency for deployment,an improved AdaTAD-based end-to-end temporal action localization ap-proach was proposed for mounting behavior in dairy goats.[Methods]The proposed approach adopted AdaTAD as the baseline end-to-end temporal action localization framework and introduced two complementary improvements,explicit key-frame guidance and multi-scale motion modelling,while retaining the original detection head and post-processing pipeline for generating temporal action in-stances.First,visual prompt tuning(VPT)was incorporated to provide task-conditioned guidance to backbone feature extraction in a parameter-efficient manner.Specifically,a small number of learnable prompt tokens were inserted into the Transformer backbone with backbone parameters frozen.Through multi-head attention interactions between prompt tokens and patch tokens,the prompts steered attention towards mounting-relevant temporal regions,strengthened feature responses at critical frames and in boundary neighbour-hoods,and improved the separability between brief target segments and abundant background frames.Second,a multi-scale motion adapter(MSMA)was introduced to model motion patterns at different temporal scales and improve robustness to diverse scene dy-namics.MSMA employed parallel multi-scale temporal depthwise separable convolution branches to capture short-,mid-,and longer-range temporal variations,enhancing representations of subtle short-duration micro-actions as well as relatively complete action pro-cesses.Residual connections and nonlinear mappings further stabilised feature injection and gradient propagation,enabling multi-scale dynamics to be integrated into backbone features with limited additional optimisation burden.Overall,VPT focused on bound-ary-relevant attention guidance,whereas MSMA emphasised multi-scale temporal dynamics modelling;Together,they formed a com-plementary design within the end-to-end localization pipeline.[Results and Discussions]Comparative experiments showed that the pro-posed method achieves an average mAP(mean Average Precision@[0.3:0.1:0.7])of 81.72%,improving upon the baseline AdaTAD by 5.00 percentage points,indicating that incorporating VPT and MSMA enhanced overall localization performance.At a temporal Inter-section over Union(tIoU)threshold of 0.7,the proposed method attained 68.85%,exceeding AdaTAD by 4.06 percentage points,dem-onstrating that the performance gain was preserved under stricter temporal boundary-consistency criteria.Further comparisons with representative approaches,including TadTR,VSGN,AFSD,ActionFormer,TriDet,DyFADet,and Re2TAL,showed average mAP im-provements of 38.82,33.83,25.29,4.09,2.83,1.20,and 6.06 percentage points,respectively,demonstrating stronger overall competi-tiveness.In terms of efficiency,the model ran at 65.78 f/s with 27.941 million trainable parameters,indicating that the accuracy gains were achieved while maintaining a relatively low parameter overhead and practical runtime efficiency.Overall,task-guided prompting and multi-scale temporal modelling improved key temporal feature representations with limited parameter increments,thereby benefit-ing localization of short,sporadic behaviors.[Conclusions]By combining VPT for boundary-relevant attention guidance with an MS-MA for multi-scale temporal dynamics modelling,the proposed AdaTAD-based end-to-end temporal action localization method im-proves localization accuracy and maintains stable advantages under stricter boundary-consistency requirements,while preserving prac-tical inference efficiency.The method could provide temporal information for reproductive behavior monitoring and decision support,and offer a feasible basis for building individual-level,time-resolved management systems in real farming environments.关键词
爬跨行为/时序动作定位/AdaTAD/精准养殖/多尺度运动建模/端到端模型/智慧养殖Key words
mounting behavior/temporal action localization/AdaTAD/precision livestock farming/multi-scale motion modeling/end-to-end model/smart breeding分类
农业科技引用本文复制引用
王佳源,李其同,罗元滔,杨蜀秦,王振华,宁纪锋,王美丽..基于改进AdaTAD的奶山羊爬跨行为时序动作定位方法[J].智慧农业（中英文）,2026,8(4):148-163,16.基金项目
国家重点研发计划项目(2022YFD1300200) （2022YFD1300200）
陕西秦创原引用高层次创新创业人才项目(QCYRCXM-2022-359) National Key Research and Development Program of China(2022YFD1300200) （QCYRCXM-2022-359）
Shaanxi Qinchuangyuan High-lev-el Innovation and Entrepreneurship Talent Program(QCYRCXM-2022-359) （QCYRCXM-2022-359）
