Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Abstract EN

Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies.

In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes.

Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements.

Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns.

Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns.

Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events.

The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods.

The experimental results show its competitive performance for anomaly event detection in video surveillance.