A two-stream abnormal detection using a cascade of extreme learning machines and stacked auto encoder

Identifying anomalous activity is a heavy task, and this has led to the progression in the domain of deep learning for video surveillance. With the development of deep learning, anomaly detection techniques have been widely used to improve the performance of various applications, including vision detection systems. However, it is still difficult to apply them directly to practical applications which usually involve the lack of abnormal samples and diversity. This paper proposes a novel Stacked Auto Encoder (SAE) and Extreme Learning Machine (ELM) abnormal detection framework based on multiples features. These features are connected to speed of movement and appearance and fed to a new neural network architecture as temporal and spatiotemporal streams. The use of ELM algorithms with an exceptionally fast learning speed when dealing with abnormal activity localization problems in addition to excellent generalization abilities, a deep learning network achieves a good performance with quick learning speed to further improve the regression performance. The strength of our proposed approaches is demonstrated by experiments with measured abnormal activities’ data. This approach can accurately identify and precisely locate abnormal events.