Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes
This work addresses the problem of detecting abnormal behaviors in crowded scenes for surveillance and security applications, presenting an incremental improvement over existing methods.
The paper tackled anomaly detection in crowded video scenes by proposing a fully convolutional neural network (FCN) method that transfers a pre-trained supervised FCN into an unsupervised one, achieving high speed and accuracy with improved detection and localization results on benchmarks.
The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.