A deep learning based methodology for video anomaly detection in crowded scenes

Different approaches have been proposed in the literature for anomaly detection in image/video area. Traditional methods such as trajectory or spatio-temporal based techniques rely on hand-crafted features. The occlusion problems and high complexity in crowded scenes are the vital drawbacks behind using these methodologies. Deep learning structures proved recently to be useful for defining effective solutions for anomaly detection where the high level features are learnt and selected automatically. However, the block-wise methods such as CNNs are computationally slow. On the other hand, they are totally supervised learning methodologies, while the video-based anomaly detection is an unsupervised problem. Auto- Encoders (Convolutional AE, vibrational AE, etc.) can be considered as an alternative option. This paper presents a stateof- the-art deep learning algorithm to be applied in such an unsupervised problem. Using the basic concepts behind the Auto-Encoders as a well-known unsupervised learning algorithm, we propose a novel methodology to detect and localize the anomalies in a video scene. The presented network is trained based on the normal patterns during training phase. The proposed structure enables the system to capture the 2D structure in image sequences during the learning process. The working hypothesis is that a deep network is able to learn normal events in videos, and, therefore, the difference of normal and anomalous frames can be used for devising an anomaly score. The simulation results on well-known data sets such as UCSD confirm that the proposed methodology achieves high performance results in case of accuracy and total processing time compared with counterpart approaches.

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