Anomalous Behaviors Detection in Moving Crowds Based on a Weighted Convolutional Autoencoder-Long Short-Term Memory Network

摘要

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.