基于深度学习的暴力行为识别研究
Research on Violent Action Recognition Based on Deep Learning
黄博 1张捷 1吕明1
作者信息
- 1. 南京理工大学自动化学院,江苏 南京 210094
- 折叠
摘要
暴力行为识别是视频行为识别领域研究的一个研究方向.随着深度学习的发展,视频行为识别在过去十年里取得了显著的进步,但也面临了新的挑战,例如如何有效地建模视频中的长时序信息、如何降低计算开销等.针对此问题,引入了一种基于软注意力机制和深度可分离卷积LSTM的深度学习网络模型,称为Att-SepConvLSTM,首先利用轻量级的NasNetMobile网络进行视频帧的空间特征提取,然后将空间特征图依次输入进去,得到全局时序特征,最后经过分类层输出是否存在暴力行为的二分类结果.
Abstract
Violent action recognition is a research direction in the field of video action recognition.With the development of deep learning,video action recognition has made significant progress in the past decade,but also faced new chal-lenges,such as how to effectively model the long-term temporal information in videos,how to reduce the computational cost,etc.To address this problem,a deep learning network model based on soft attention mechanism and depthwise sep-arable convolution LSTM,called Att-SepConvLSTM,is introduced.It first uses a lightweight NasNetMobile network to extract the spatial features of video frames,then inputs the spatial feature maps sequentially,obtaining the global temporal fea-tures,and finally outputs whether there is violent action or not through a classification layer.
关键词
暴力行为识别/深度学习/注意力机制/深度可分离卷积LSTMKey words
violent action recognition/deep learning/soft attention mechanism/depthwise separable convolution LSTM引用本文复制引用
出版年
2024
NETL
NSTL
万方数据