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A Spatial-Temporal Normalized Contrastive Embedding for Robust Motion Similarity Retrieval 认领 引用
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作者 Seung-su Lee Young-Been Noh +1 位作者 Hwa Young Jeong Kwang-il Hwang 《Computers, Materials & Continua》 SCIE EI 2026年第9期127-158,共32页
Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation,viewpoint inconsistency,translation drift,and temporal misalignment.Existing contrastive skeleton learning meth... Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation,viewpoint inconsistency,translation drift,and temporal misalignment.Existing contrastive skeleton learning methods primarily address action recognition and rarely integrate explicit geometric canonicalization for retrievaloriented metric learning.This paper proposes a spatial-temporal normalized contrastive embedding framework that unifies structured nuisance suppression with scalable similarity representation learning.A four-stage normalization pipeline—torso-scale normalization,pelvis-centered alignment,posture-axis alignment,and phase-synchronized temporal resampling—removes geometric and temporal distortions prior to embedding.The normalized sequences are encoded using an acausal dilated temporal convolutional network trained with a hybrid contrastive objective combining NT-Xent and semi-hard triplet loss,enabling both global separation and fine-grained stylistic discrimination.A prototype-based representation further supports interpretable amateur-to-professional style mapping.Experiments on a golf swing benchmark achieve a Top-1 accuracy of 91.3%,outperforming BiLSTM and Dynamic Time Warping baselines.The framework establishes an invariant and interpretable paradigmfor motion similarity retrieval applicable to broader human movement analysis tasks. 展开更多
关键词 Motion similarity retrieval spatial-temporal normalization contrastive representation learning temporal convolutional networks(TCN) prototype-based embedding
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