2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-1.8
Paper Title HIGH-ORDER JOINT INFORMATION INPUT FOR GRAPH CONVOLUTIONAL NETWORK BASED ACTION RECOGNITION
Authors Wen-Nung Lie, Yong-Jhu Huang, Jui-Chiu Chiang, Zhen-Yu Fang, National Chung Cheng University, Taiwan
SessionMLR-APPL-IVASR-1: Machine learning for image and video analysis, synthesis, and retrieval 1
LocationArea D
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Graph Convolution Network (GCN)-based networks for human action recognition, accepting 3D skeleton sequence as input, have gained much attention and good performances recently. In this paper, joint information enhanced with rich higher-order features/attributes is proposed to lift up their recognition performances. All joints in a spatio-temporal skeleton are described in terms of a set of 3-component vectors by referring up to 3 joint neighbors in the spatio-temporal domain. The referred joints are physically connected in spatial or corresponded in temporal domain. Our rich high-order joint information is fed as inputs to two kinds of GCN-based networks in two ways: early fusion and late fusion. Early fusion is to concatenate these 3-components vectors as different channels at input nodes and late fusion is to feed each 3-component vector to a multi-stream GCN network separately and then fuse the output from each stream for action recognition decision. We also propose to cascade a view-adaptive (VA) sub-network to further promote the performance. Experiments show that our approach is capable of boosting the accuracy of original GCN networks in both early or late fusion styles by up to 1.57% and 2.55%, respectively (in cross-subject (CS) protocol) when using NTU RGB-D 60 dataset for evaluations.