We propose an anomaly detection approach by learning a generative model of moving pedestrians to guarantee public safety. To resolve the existing challenges of anomaly detection in complicated definitions, complex backgrounds, and local occurrence, a weighted convolutional autoencoder-long short-term memory network is proposed to reconstruct raw data and their corresponding optical flow and then perform anomaly detection based on reconstruction errors. Unlike equally treating raw data and optical flow, a novel two-stream framework is proposed to take the reconstructed optical flow as supplementary cues that encode pedestrian motions. A weighted Euclidean loss is proposed to enable the network to concentrate on moving foregrounds, thus restraining background influence. Global-local analysis is used to jointly detect and localize local anomaly in reconstructed raw data. Final detection of anomalous events is achieved by jointly considering the results of the global-local analysis and reconstructed optical flow. Qualitative evaluations verify the effectiveness of our two-stream framework, the weighted Euclidean loss, and the global-local analysis. Moreover, comparisons with state-of-the-art approaches indicate the superiority of our approach in anomaly detection.

中文翻译：

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基于加权卷积自编码器-长短期记忆网络的移动人群异常行为检测

我们通过学习移动行人的生成模型来提出一种异常检测方法，以保证公共安全。为了解决定义复杂、背景复杂、局部发生的异常检测的现有挑战，提出了一种加权卷积自编码器-长短期记忆网络来重建原始数据及其相应的光流，然后基于重建误差进行异常检测. 与同等对待原始数据和光流不同，提出了一种新颖的双流框架，将重建的光流作为编码行人运动的补充线索。提出了加权欧几里德损失，使网络能够专注于移动前景，从而抑制背景影响。全局-局部分析用于联合检测和定位重建原始数据中的局部异常。异常事件的最终检测是通过联合考虑全局-局部分析和重建光流的结果来实现的。定性评估验证了我们的双流框架、加权欧几里得损失和全局-局部分析的有效性。此外，与最先进方法的比较表明我们的方法在异常检测方面的优越性。