Anomaly detection based on Nearest Neighbor search with Locality-Sensitive B-tree

With the increasing demand of security and safety assurance for public, anomaly detection has gained a greater focus in the field of intelligent video surveillance analysis. In this paper, a novel method is proposed to address the issue in anomaly detection. It is based on Nearest Neighbor (NN) search with the Locality-Sensitive B-tree (LSB-tree), which helps to find the approximate NNs among the normal feature samples for each test sample. To better analyze the pedestrian behavior, not only the commonly used motion-appearance feature is applied in the method, but also a novel feature is proposed to describe the dynamic changes of the behavior. Compared to the relative works, the main novelties of this paper mainly includes: (1) the method of LSB-tree, which enables fast high-dimensional NN search, is applied in this paper to evaluate the similarity between the test samples and normal feature samples; (2) in order to analyze the dynamic motion and appearance, the Dynamics of Pedestrian Behavior (DoPB) feature on Riemannian manifolds is applied as the individual descriptor, which helps to detect the drastic behaviors and abnormal translation motions; (3) a new evaluation method is developed to generate the anomaly map and determine the anomaly. Experimental results and the comparisons with state-of-the-art methods demonstrate that the proposed method is effective in anomaly detection and localization, and is applicable in various scenes. (C) 2018 Elsevier B.V. All rights reserved.