三维卷积神经网络及其在视频理解领域中的应用研究

白静; 杨瞻源; 彭斌; 李文静

doi:10.11999/JEIT220596

三维卷积神经网络及其在视频理解领域中的应用研究

doi: 10.11999/JEIT220596

白静^{1, 2},
杨瞻源^1, ,,
彭斌¹,
李文静¹

1.
北方民族大学计算机科学与工程学院银川 750021
2.
国家民委图像图形智能处理实验室银川 750021

基金项目: 国家自然科学基金(62162001, 61762003)，宁夏自然科学基金(2022AAC02041)，宁夏优秀人才支持计划，北方民族大学创新项目(YCX22194)

详细信息

作者简介:
白静：女，教授，硕士生导师，研究方向为机器学习、深度表征学习、计算机视觉应用

杨瞻源：男，硕士生，研究方向为图像处理与计算机视觉、深度表征学习

彭斌：男，硕士生，研究方向为图像处理与计算机视觉、深度表征学习

李文静：女，硕士生，研究方向为图像处理与计算机视觉、深度表征学习

通讯作者:
杨瞻源　1273907064@qq.com

中图分类号: TP399
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-11
- 修回日期: 2022-11-18
- 网络出版日期: 2022-11-21
- 刊出日期: 2023-06-10

Research on 3D Convolutional Neural Network and Its Application to Video Understanding

BAI Jing^{1, 2},
YANG Zhanyuan^{1
, ,},
PENG Bin¹,
LI Wenjing¹

1.
School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
2.
National Ethnic Affairs Commission Image Graphics Intelligent Processing Laboratory, Yinchuan 750021, China

Funds: The National Natural Science Foundation of China (62162001, 61762003), The Natural Science Foundation of Ningxia Province of China (2022AAC02041), The CAS “Light of West China” Program, The Ningxia Excellent Talent Program, North Minzu University Innovation Project(YCX22194)

摘要

摘要: 3维卷积神经网络(3D CNN)是近几年来深度学习研究中的热点，在计算机视觉领域取得了诸多成就。虽然研究多年且成果丰富，但目前仍缺少关于此内容全面、细致的综述。基于此，该文从以下几个方面对其进行综述：首先阐述3维卷积神经网络的基本原理和模型结构，接着从网络结构、网络内部和优化方法总结3维卷积神经网络的相关改进工作，然后对3维卷积神经网络在视频理解领域中的应用进行总结，最后总结全文内容并对未来发展方向进行展望。该文针对3维卷积神经网络的最新研究进展以及在视频理解领域中的应用进行了系统的综述，对3维卷积神经网络的研究发展具有一定的积极意义。
- 视频理解 /
- 深度学习 /
- 3维卷积神经网络 /
- 网络结构
Abstract: 3D Convolutional Neural Network (3D CNN) has been a hot topic in deep learning research over the last few years and has made great achievements in computer vision. Despite years of research and abundant results, a comprehensive and detailed review of this content is still lacking. In this paper, the 3D convolutional neural network is introduced in the following aspects. Firstly, the rationale and model structure of 3D convolutional neural network are put forward. Then the improvement of 3D convolutional neural network is summarized from the network structure, network interior and optimization methods. After that the application of 3D convolutional neural network to the field of video understanding is explained. Finally, the contents summary of the paper and future development. This paper provides a systematic review of the latest research progress of 3D convolutional neural networks and their applications in the field of video understanding, which is of positive significance to the research and development of 3D convolutional neural network.
- Video understanding /
- Deep learning /
- 3D Convolutional Neural Network (3D CNN) /
- Network structure

HTML全文

图 1 3D CNN网络模型改进思路

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图 2 网络深度方向的改进

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图 3 网络宽度方向的改进

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图 4 卷积层的改进

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图 5 3D CNN在行为识别任务中的应用

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表 1 常用的行为识别数据集

数据集	类别数	视频数	训练集	测试集	动作类型
UCF-101^[35]	101	13320	9324	3996	人物交互、肢体动作、人人交互、乐器演奏、体育运动
HMDB-51^[36]	51	6766	4736	2030	常见/复杂的面部动作、常见/复杂的肢体动作、多人交互动作
Kinetics400^[37]	400	254380	234619	19761	人物交互和人人交互
Sports-1M^[38]	487	1133158	793211	339947	运动视频

下载: 导出CSV

表 2 行为识别任务中不同3D CNN在不同数据集上的性能对比(表内数据源于相关论文)

改进角度	年份	网络	不同数据集上的准确率(%)				参数量 (M)	计算速率 (VPS/GFLOPs)
改进角度	年份	网络	UCF-101	HMDB-51	Kinetics400	Sports-1M	参数量 (M)	计算速率 (VPS/GFLOPs)
基础结构	2015	C3D	82.3	40.4	–	85.2	33.4	–	/	–
残差连接	2017	Res3D	85.8	54.9	–	87.8	33.2	0.9	/	–
残差连接	2021	R-M3D	93.2	65.4	–	–	–	–	/	–
卷积核拆分	2017	P3D	88.6	–	–	87.4	–	<2.0	/	–
	2018	R(2+1)D	97.3	78.7	75.4	91.9	33.3	–	/	–
	2018	S3D-G	96.8	75.9	76.2	–	11.6	–	/	71.4
3维膨胀卷积	2017	I3D	93.4	66.4	72.6	–	25.0	–	/	107.9
2D+3D	2018	ARTNet	93.5	67.6	72.4	–	33.4	2.9	/	20.0
2D+3D	2018	ECO	94.8	72.4	–	–	–	28.2	/	–
多支路	2022	3D Dual-Stream-SRU	95.3	76.5	–	–	–	–	/	–
知识蒸馏	2020	D3D	97.6	80.5	75.9	–	–		/
注意力模块	2021	EAM+ResNet50	89.8	65.4	–	–	46.3	–	/	10.1
注意力模块	2021	DA+ResNext101	95.8	74.3	–	–	–	–	/	–

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