Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
Sensors (Basel). 2021 Nov 12;21(22):7508. doi: 10.3390/s21227508.
Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.
弱监督视频异常检测是计算机视觉研究的一个新热点，这要归功于大规模弱监督视频数据集的可用性。然而，大多数现有的研究工作仅限于基于帧的分类，重点是寻找特定对象或活动的存在。本文提出了一种新的神经网络架构，用于有效地提取突出特征，以检测视频是否存在异常。将视频视为整体输入，检测遵循视频标签分配的过程。通过三维卷积进行空间和时间特征的提取，然后使用 LSTM 网络进一步对其关系进行建模。所提出方法的简洁结构实现了高计算效率，广泛的实验证明了其有效性。
