Location Aware Hierarchical Cell-Based Anomaly Detection and Categorization in Crowded Scenes

In this paper, we propose a novel framework for anomaly detection and categorization in crowded scenes via location aware hierarchical cell-based (LAHCB) approach. Input video frames are split into spatio-temporal volumes (STVs). Low-level histogram of optical flow orientation and motion magnitude features are extracted from selective STVs (SSTVs) at three levels of hierarchy achieving a coarse-to-the-fine localization. We use one-class SVM (OCSVM) classifier to model normal events and detect anomaly. The proposed method consists of two parts: (1) Global Analysis for frame-level detection; (2) Local Analysis for pixel-level localization. For global analysis, we adopt computationally less expensive model that uses only coarser level. Further, we use this information to achieve localization. The computational efficiency lies in faster online testing since storage and time overhead is due to offline feature extraction and classification at different levels in the hierarchical structure during training phase. Unlike existing methods, the proposed approach omits pixel level feature computation and background modeling. The addition of location aware concept detects abnormal behaviour in an unexpected region. We demonstrate the performance of the proposed method on the UCSD and UMN datasets. We achieve AUC of 86.16% and 86.3% on Ped1 and Ped2, comparable with state-of-the-art methods.