深度学习在视频动作识别中的应用
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引用本文:潘陈听?覮.深度学习在视频动作识别中的应用[J].计算技术与自动化,2020,(4):123-127
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作者单位
潘陈听?覮 (南京航空航天大学 计算机科学与技术学院江苏 南京 211106) 
中文摘要:快速有效地识别出视频中的人体动作,具有极其广泛的应用前景及潜在的经济价值,深度学习的火热给视频动作自动识别带来了巨大的发展。提出了一种基于深度学习和非局域平均法的自注意时间段网络,作用于剪切好的视频片段。通过构造非局域模块并将其加入到以ResNet为基本模型的时间段网络,可以得到新模型。经过在TDAP数据集上验证,该模型可较为精确地识别出人体动作,与原有模型相比在不增加时间复杂度的前提下有一定程度的提升。
中文关键词:动作识别  非局域模块  时间段网络
 
Application of Deep Learning in Video Action Recognition
Abstract:Recognizing human actions in videos quickly and effectively,has broad application prospects and potential economic value. Deep learning has been widely used for action recognition. We proposed self-attention temporal segment networks,whose inputs are clipped video clips. This network is based on deep networks and non-local means. By adding non-local modules to temporal segment networks with ResNet as the basic model,we can get our new model. Verified on TDAP dataset,our new model can recognize human actions more accurately than the original model,without increasing much time complexity.
keywords:action recognition  non-local module  temporal segment network
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