Part-based Graph Convolutional Network for Action Recognition

Kalpit Thakkar; P J Narayanan

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arXiv · 1809.04983

Part-based Graph Convolutional Network for Action Recognition

Kalpit Thakkar·P J Narayanan

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Abstract

Human 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.

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BibTeX RIS

Kalpit Thakkar, P J Narayanan. 2018-09-13. Part-based Graph Convolutional Network for Action Recognition. https://arxiv.org/abs/1809.04983

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