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.
翻译:针对当前视频异常探测无法充分利用时间信息而忽视正常行为多样性的问题,建议采用异常检测方法,整合行人时空信息。根据同步自动编码器,输入框架通过编码器和解码器压缩并恢复。根据输出框架与真实价值之间的差异,异常检测得以实现。为了加强连续视频框架、剩余时间转移模块和剩余频道关注模块之间的特征信息连接,引入了异常检测方法,以分别提高网络在时间信息和频道信息方面的建模能力。由于聚合神经网络在记忆增强模块中过于普遍化,添加了每个编码层的新建连接,以限制自动编码器代表异常框架的能力,提高网络异常检测的准确性。此外,目标功能还因特征离散损失而改变,从而有效地区分了不同的正常行为模式。CUHK大道和上海科技数据集的实验结果显示,在满足实时视频异常探测方法的同时,拟议方法优于当前主流视频异常现象探测方法。