1. 基于深度学习的轻量型人体动作识别模型.
- Author
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何冰倩, 魏 维, and 张 斌
- Subjects
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ARTIFICIAL neural networks , *CONVOLUTIONAL neural networks , *HUMAN behavior , *HUMAN activity recognition , *DEEP learning , *IMAGE processing - Abstract
Aiming at the problems that the existing human motion recognition methods based on deep learning have large parameters and the networks were too deep and heavy, this paper proposed a lightweight two-steam fusion deep neural network model and applied this model to human action recognition. This model combined a shallow multi-scale network with a deep network, and achieved a significant reduction in the amount of model parameters and avoided the problem that network was too deep. Experiments were performed on datasets UCFlOl and HMDB51, achieving 94.0% and 69.4% recognition accuracy in ImageNet pre-training mode, respectively. Experiments show that compared with the existing human motion recognition models based on deep learning, this model greatly reduces the parameter quantity and still has high motion recognition accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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