论文标题
通过预测网络进行视频异常检测，并具有增强的时空存储器交换
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange
论文作者
论文摘要
视频异常检测是一项具有挑战性的任务，因为大多数异常都是稀缺和非确定性的。许多方法研究了正常模式和异常模式之间的重建差异，但是忽略了异常不一定与大重建误差相对应。为了解决此问题，我们设计了使用Bi Directionality和高阶机制来增强时空存储器交换的卷积LSTM自动编码器预测框架。双向结构通过前进和向后的预测促进了学习时间的规律性。独特的高阶机制进一步加强了编码器和解码器之间的空间信息相互作用。考虑到卷积LSTMS中有限的接收场，我们还引入了一个注意模块，以突出预测的信息特征。最终通过将框架与它们的相应预测进行比较来确定异常。对三个流行基准的评估表明，我们的框架的表现优于大多数基于预测的异常检测方法。
Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not necessarily correspond to large reconstruction errors. To address this issue, we design a Convolutional LSTM Auto-Encoder prediction framework with enhanced spatio-temporal memory exchange using bi-directionalilty and a higher-order mechanism. The bi-directional structure promotes learning the temporal regularity through forward and backward predictions. The unique higher-order mechanism further strengthens spatial information interaction between the encoder and the decoder. Considering the limited receptive fields in Convolutional LSTMs, we also introduce an attention module to highlight informative features for prediction. Anomalies are eventually identified by comparing the frames with their corresponding predictions. Evaluations on three popular benchmarks show that our framework outperforms most existing prediction-based anomaly detection methods.
