论文标题
视频异常检测的行人时空信息融合
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection
论文作者
论文摘要
目的是针对当前视频异常检测无法完全使用时间信息并忽略正常行为的多样性的问题，提出了一种异常检测方法来整合行人的时空信息。基于卷积自动编码器，输入框架通过编码器和解码器进行压缩和还原。根据输出框架和真实值之间的差异来实现异常检测。为了加强连续视频帧之间的特征信息连接，介绍了剩余的时间偏移模块和残留的通道注意模块，以提高网络在时间信息和通道信息上的建模能力。由于卷积神经网络过多概括，在内存增强模块中，添加了每个编解码器层的跳跃连接，以限制自动编码器过于剧烈表示异常帧的能力，并提高网络的异常检测准确性。此外，目标函数通过特征离散化损失来修改，该损失有效地区分了不同的正常行为模式。 CUHK Avenue和Shanghaitech数据集的实验结果表明，该方法在满足实时要求的同时，提出的方法优于当前主流视频异常检测方法。
Aiming at the problem that the current video anomaly detection cannot fully use the temporal information and ignore the diversity of normal behavior, an anomaly detection method is proposed to integrate the spatiotemporal information of pedestrians. Based on the convolutional autoencoder, the input frame is compressed and restored through the encoder and decoder. Anomaly detection is realized according to the difference between the output frame and the true value. In order to strengthen the characteristic information connection between continuous video frames, the residual temporal shift module and the residual channel attention module are introduced to improve the modeling ability of the network on temporal information and channel information, respectively. Due to the excessive generalization of convolutional neural networks, in the memory enhancement modules, the hopping connections of each codec layer are added to limit autoencoders' ability to represent abnormal frames too vigorously and improve the anomaly detection accuracy of the network. In addition, the objective function is modified by a feature discretization loss, which effectively distinguishes different normal behavior patterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets show that the proposed method is superior to the current mainstream video anomaly detection methods while meeting the real-time requirements.
