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.
@cite_35: In this paper, we present a novel approach for video-anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity-pattern discovery framework, comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised methods for automatically constructing normal activity patterns at different levels. A unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the effectiveness of the proposed method on the UCSD anomaly-detection datasets and compare the performance with existing work.
@cite_63: In this paper we present a novel method for dominant motion analysis in crowded scenes, based on corner features. In our method, we initialize corner features on the scene, and advect them through optical flow. Approximating the moving corner features to individuals, their interaction forces, represented as endothermic reactions in a thermodynamic system, are computed using the enthalpy measure, thus obtaining the potential corner features of interest. These features are exploited to extract the orientation patterns, used as input priors for training a random forest. The experimental evaluation is conducted on a set of benchmark video sequences, commonly used for crowd motion analysis, and the obtained results are compared against other state of the art techniques.
Authors of @cite_63 extract corner features and analyze them based on their properties of motion by an enthalpy model, a random forest with corner features for detecting abnormal samples. @cite_35 propose a unified anomaly energy function based on a hierarchical activity-pattern discovery for detecting anomalies.