结合自适应局部图卷积与多尺度时间建模的骨架行为识别

打开文本图片集

关键词：局部图卷积；自适应图；多尺度时间建模；行为识别

中图分类号：TP391.41 文献标志码：A 文章编号：1001-3695（2025）07-037-2199-07

doi：10. 19734/j. issn. 1001-3695.2024.08.0370

Abstract：Giventheinherent topologicalstructurecharacteristicsofthehumanskeletonresearchersefectivelymodelskeleton datausing graph convolution networks forbehaviorrecognition.However，chalenges arisein skeleton behaviorrecognition methods becausetimeconvolutionreliesonafixedtopological graphstructureandfixed kemel size，whichmakes itdificult to adapttovariableactiontypes，osures，andbehavioraldurations.Thisrelianceleads to modeling erorsandafectsrecogition accuracy.To tacklethis isue，this paper proposed a skeleton behaviorrecognitionmethodthatcombined adaptivelocal graph convolutionwithmulti-saletemporalmodeling.Thismethodalowedfortheindependentdynamiccharacterizationoftheuman skeletalstructurethroughtheadaptivelocal graphconvolutionmodule.Itdesignedthemulti-scaletemporalmodeling moduleto accommodatebehaviorsofvaryingdurationswhilereducing thenumberof parametersandcomputational complexity.Furthermore，itintroducedthespatio-temporalDropGraphstructuretodynamicalladjustthegraphtopology，whichimprovedthe model's generalization ability and prevents overfiting. The experiments show that it achieves accuracy rates of 93.39% and 97.18% under the cross-object C-Sub and cross-view C-View benchmarks for the NTU RGB+D60 dataset，respectively，and （20 90.48% and 91.95% under the cross-object C-Sub and cross-set C-Set benchmarks for the NTU RGB+D 120 dataset，respectively.Theseresultsoutperformthoseofexisting behavioralrecognitionmethods，proving thesuperiorityof theapproach.

Key words：local graph convolution；adaptive graph；multi-scale time modeling；behavior recognition

0引言

作为计算机视觉领域的核心课题之一，行为识别在虚拟现实、智能家居和自动驾驶等多个领域中显示出重要性和广阔的应用潜力。（剩余18789字）