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计算机系统应用英文版:2021,30(3):272-275
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基于深度学习的多模态时空动作识别
(河海大学 计算机与信息学院, 南京 211100)
Multi-Modal Spatiotemporal Action Recognition Based on Deep Learning
(College of Computer and Information, Hohai University, Nanjing 211100, China)
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Received:July 19, 2020    Revised:August 28, 2020
中文摘要: 针对视频理解中的时序难点以及传统方法计算量大的困难, 提出了一种带有时空模块的方法用于动作识别. 该方法采用残差网络作为框架, 加入时空模块提取图像以及时序信息, 并且加入RGB差值信息增强数据, 采用NetVLAD方法聚合所有的特征信息, 最后实现行为动作的分类. 实验结果表明, 基于时空模块的多模态方法具有较好的识别精度.
中文关键词: 时空模型  多模态  动作识别
Abstract:In view of the time-series difficulty in video understanding and a large amount of calculation in traditional methods, we propose a method with spatio-temporal module for action recognition. With a residual network as the framework, this method adds spatio-temporal module to extract images and time series, adds RGB difference to enhance data, and finally uses the NetVLAD method to aggregate all feature information. In this way, actions are classified. The experimental results show that the multimodal method based on spatio-temporal module has better recognition accuracy.
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吴敏,王敏.基于深度学习的多模态时空动作识别.计算机系统应用,2021,30(3):272-275
WU Min,WANG Min.Multi-Modal Spatiotemporal Action Recognition Based on Deep Learning.COMPUTER SYSTEMS APPLICATIONS,2021,30(3):272-275