Zhou Xiang, Cui Yue, Xu Gang, Chen Hongliang, Zeng Jing, Li Yutong, Xiao Jiangjian
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.
Computer Vision Laboratory, Advanced Manufacturing Institute, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315211, China.
J Imaging. 2023 Mar 7;9(3):60. doi: 10.3390/jimaging9030060.
In order to solve the problem of long video dependence and the difficulty of fine-grained feature extraction in the video behavior recognition of personnel sleeping at a security-monitored scene, this paper proposes a time-series convolution-network-based sleeping behavior recognition algorithm suitable for monitoring data. ResNet50 is selected as the backbone network, and the self-attention coding layer is used to extract rich contextual semantic information; then, a segment-level feature fusion module is constructed to enhance the effective transmission of important information in the segment feature sequence on the network, and the long-term memory network is used to model the entire video in the time dimension to improve behavior detection ability. This paper constructs a data set of sleeping behavior under security monitoring, and the two behaviors contain about 2800 single-person target videos. The experimental results show that the detection accuracy of the network model in this paper is significantly improved on the sleeping post data set, up to 6.69% higher than the benchmark network. Compared with other network models, the performance of the algorithm in this paper has improved to different degrees and has good application value.
为解决安防监控场景下人员睡眠视频行为识别中存在的长视频依赖问题以及细粒度特征提取困难的问题，本文提出一种适用于监控数据的基于时间序列卷积网络的睡眠行为识别算法。选用ResNet50作为骨干网络，利用自注意力编码层提取丰富的上下文语义信息；然后构建片段级特征融合模块，增强片段特征序列中重要信息在网络上的有效传递，并利用长短期记忆网络在时间维度上对整个视频进行建模，以提高行为检测能力。本文构建了安防监控下的睡眠行为数据集，两种行为包含约2800个单人目标视频。实验结果表明，本文网络模型在睡眠姿态数据集上的检测准确率有显著提高，比基准网络高出6.69%。与其他网络模型相比，本文算法的性能有不同程度的提升，具有良好的应用价值。
