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• W2752968997 abstract "Action recognition using depth sequences plays important role in many fields, e.g., intelligent surveillance, content-based video retrieval. Real applications require robust and accurate action recognition method. In this paper, we propose a skeleton visualization method, which efficiently encodes the spatial-temporal information of skeleton joints into a set of color images. These images are served as inputs for convolutional neural networks to extract more discriminative deep features. To enhance the ability of deep features to capture global relationships, we extend the color images into multi-temporal version. Additionally, to solve the effect of view point changes, a spatial transform method is adopted as a preprocessing step. Extensive experiments on NTU RGB+D dataset and ICME2017 challenge show that our method can accurately distinguish similar actions and shows robustness to view variations." @default.
• W2752968997 created "2017-09-15" @default.
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• W2752968997 date "2017-07-01" @default.
• W2752968997 modified "2023-09-22" @default.
• W2752968997 title "3D action recognition using multi-temporal skeleton visualization" @default.
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• W2752968997 doi "https://doi.org/10.1109/icmew.2017.8026280" @default.
• W2752968997 hasPublicationYear "2017" @default.
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