计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 139-147.doi: 10.11896/JsJkx.190900176
赫磊, 邵展鹏, 张剑华, 周小龙
HE Lei, SHAO Zhan-peng, ZHANG Jian-hua and ZHOU Xiao-long
摘要: 行为识别是计算机视觉领域的基本问题之一,基于深度学习的行为识别算法是当前行为识别的主流算法。在已有的研究中,传统特征提取方法一般是通过人工观察和设计,手动设计出能够表征视频动作的特征。然而,在手工特征表达的基础上构建复杂分类模型的方法已经不能适应高识别精度和应用性的要求,而深度学习的引入为行为识别带来了新的发展方向。文中主要综述了基于深度学习的行为识别算法,首先介绍了行为识别的研究背景和意义,并分别对行为识别的传统学习方法和深度学习方法进行了介绍;然后对深度学习下的算法模型结构进行分类介绍,包括Two-Stream、3D-ConvNet、融合CNN-LSTM 3种算法模型结构;最后介绍了目前常用的公开验证数据集,并主要针对基于两种数据模态的识别算法进行了横向比较,一种是基于RGB视频的UCF101和HMDB51数据集,一种是基于人体骨架序列视频的NTU RGB+D数据集。实验结果表明:深度学习方法已经取得了很大的进步,卷积神经网络的应用极大地促进了行为识别算法的发展,逐步替代了基于手工提取特征的传统方法,尤其采用了卷积神经网络算法之后在行为数据集上的准确率有了显著提高。对于RGB视频而言,Two-Stream和3DConvNet是算法模型结构的主流,对于骨架序列视频而言,Two-Stream和融合时空图模型是算法模型结构的主流。
中图分类号:
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