Hierarchical detection of abnormal behaviors in video surveillance through modeling normal behaviors based on AUC maximization

Detection of abnormal behaviors is a challenging issue as there are disagreements over how abnormal behaviors should be defined. In this paper, a new method is proposed to estimate a model of normal behaviors and consequently to detect abnormal behaviors. Estimating a model of normal behaviors constitutes a main part of the training phase in detecting abnormal behaviors and is one of the main issues in the field. To estimate such a model, the histogram of the oriented optical flow (HOF) is first extracted as a local-based feature, and then, the proposed features of speed, variance of the trajectory and deviation from the trajectory are extracted as object-based features. Spectral clustering is used to cluster behavioral features that are similar. To design a classifier for estimating the model of normal behaviors in each cluster, a new method is proposed based on maximization of the area under the receiver operating characteristic curve (AUC). This new method does not need manual labeling for estimation of the model, and then, it carries out the estimation process in a semi-supervised fashion. In this paper, a method for hierarchical detection of abnormal behaviors based on the priority of the behavioral features is also proposed in the testing phase of the study. To this purpose, the existence of an abnormal behavior is hierarchically detected through using the HOF feature and the three object-based features of speed, variance of the trajectory and deviation from the trajectory. The distinguishing characteristic of the proposed method is that, as soon as the abnormal behavior is detected along the hierarchical application of the features, the process stops. The experimental results show that the proposed method can effectively detect the abnormal behaviors in several databases and achieve comparable performance to the state-of-the-art methods for detection of abnormal behaviors.