Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Shaonian Huang, Dongjun Huang and Xinmin Zhou

Mathematical Problems in Engineering, 2018, vol. 2018, 1-13

Abstract:

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6323942

DOI: 10.1155/2018/6323942

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