Deep learning and handcrafted features for one-class anomaly detection in UAV video

Abstract

Visual surveillance systems have recently captured the attention of the research community. Most of the proposed surveillance systems deal with stationary cameras. Nevertheless, these systems may reflect minor applicability in anomaly detection when multiple cameras are required. Lately, under technological progress in electronic and avionics systems, Unmanned Aerial Vehicles (UAVs) are increasingly used in a wide variety of urban missions. Especially, in the surveillance context, UAVs can be used as mobile cameras to overcome weaknesses of stationary cameras. One of the principal advantages that makes UAVs attractive is their ability to provide a new aerial perspective. Despite their numerous advantages, there are many difficulties associated with automatic anomalies detection by an UAV, as there is a lack in the proposed contributions describing anomaly detection in videos recorded by a drone. In this paper, we propose new anomaly detection techniques for assisting UAV based surveillance mission where videos are acquired by a mobile camera. To extract robust features from UAV videos, three different features extraction methods were used, namely a pretrained Convolutional Neural Network (CNN) and two popular handcrafted methods (Histogram of Oriented Gradient (HOG) and HOG3D). One Class Support Vector Machine (OCSVM) has been then applied for the unsupervised classification. Extensive experiments carried on a dataset containing videos taken by an UAV monitoring a car parking, prove the efficiency of the proposed techniques. Specifically, the quantitative results obtained using the challenging Area Under Curve (AUC) evaluation metric show that, despite the variation among them, the proposed methods achieve good results in comparison to the existing technique with an AUC = 0.78 at worst and an AUC = 0.93 at best.