IIIT Hyderabad Publications |
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Part-based Graph Convolutional Network for Action RecognitionAuthors: Kalpit Thakkar,P J Narayanan Conference: British Machine Vision Conference (BMVC) (BMVC-2018 2018) Location New Castle, UK Date: 2018-09-03 Report no: IIIT/TR/2018/135 AbstractHuman actions comprise of joint motion of articulated body parts or “gestures”. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks have been used to recognize actions from skeletal videos. We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network. We show that such a model improves performance of recognition, compared to a model using entire skeleton graph. Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance. Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition. Full paper: pdf Centre for Visual Information Technology |
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