REMOVED: Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency

Abstract

Abstract Unmanned Aerial Vehicles (UAV) can be used to great effect for the purposes of surveillance or search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data (typically video) which must be analyzed before any potential findings can be uncovered and actions taken. This is a slow and expensive process which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem, it is proposed a deep model using a visual saliency approach to automatically analyze and detect anomalies in UAV video. Contextual Saliency for Anomaly Detection in UAV Video (CSADUV) model is based on the state-of-the-art in visual saliency detection using deep convolutional neural networks and considers local and scene context, with novel additions in utilizing temporal information through a convolutional LSTM layer and modifications to the base model. This model achieves promising results with the addition of the temporal implementation producing significantly improved results compared to the state-of-the-art in saliency detection. However, due to limitations in the dataset used the model fails to generalize well to other data, failing to beat the state-of-the-art in anomaly detection in UAV footage. The approach taken shows promise with the modifications made yielding significant performance improvements and is worthy of future investigation. The lack of a publicly available dataset for anomaly detection in UAV video poses a significant roadblock to any deep learning approach to this task, however despite this paper shows that leveraging temporal information for this task, which the state-of-the-art does not currently do, can lead to improved performance.