Global abnormal events detection in crowded scenes using context location and motion-rich spatio-temporal volumes

Global abnormal events form unique and distinct motion characteristics and category of anomalies at image level rather than pixel level with less complexity compared to local abnormal events. However, traditional anomaly detection approaches focused more on pixel-level feature extraction from foreground pixels and combine global and local anomaly detection in a single algorithm with equal degree of computational complexity. In this paper, we propose a novel framework for global anomaly detection via block-level feature extraction using context location (CL) and motion-rich STVs (MRSTVs). The histogram of optical flow orientation and motion magnitude features from spatio-temporal volumes (STVs) are used as global feature descriptor to capture motion characteristics of normal and abnormal events. Simple and cost-effective one-class SVM classifier is employed to learn normal behaviour from MRSTVs during training and detect abnormal STVs from test data. Thereafter, a spatio-temporal post-processing technique detects frame-level abnormal behaviour and reduces false alarm rate. We define CL to detect abnormal behaviour in an unexpected region. The proposed approach omits pixel-level feature extraction and background modelling by considering MRSTVs, thus enhances detection rate and reduces computational complexity. We have conducted experiments on widely used UMN and PETS2009 datasets to compare the performance of proposed approach with existing methods.