Abstract
Unmanned aerial vehicles (UAV) can be used to great effect for wide-area searches such as 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 in video format, which must be analysed 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 we propose a deep model architecture using a visual saliency approach to automatically analyse and detect anomalies in UAV video. Our Temporal Contextual Saliency (TeCS) approach is based on the state-of-the-art in visual saliency detection using deep Convolutional Neural Networks (CNN) and considers local and scene context, with novel additions in utilizing temporal information through a convolutional Long Short-Term Memory (LSTM) layer and modifications to the base model architecture. We additionally evaluate the impact of temporal vs non-temporal reasoning for this task. Our model achieves improved results on a benchmark dataset with the addition of temporal reasoning showing significantly improved results compared to the state-of-the-art in saliency detection.
Highlights
Modern advances in technology have enabled the use of Unmanned Aerial Vehicles (UAV) for the purposes of surveillance and search and rescue operations, reducing the costs and improving the capabilities of such operations
In order to solve this problem we evaluate the benefit of temporal information processing for anomaly detection in UAV video, and propose a novel Temporal Contextual Saliency (TeCS) model based on the Deep Spatial Contextual Long-term Recurrent Convolutional Network (DSCLRCN) model of [5], state-of-the-art approach in saliency prediction using a deep Convolutional Neural Network (CNN)
We report the results of each model using several loss functions as performance metrics: our NSSalt score, which was used to train the TeCS model, split into positive and negative images, Cross Entropy and Mean Absolute Error (CE_MAE) based on the recommendation of [15], and our Difference of Means (DoM) score, which was used to train the NTeCS model
Summary
Modern advances in technology have enabled the use of Unmanned Aerial Vehicles (UAV) for the purposes of surveillance and search and rescue operations, reducing the costs and improving the capabilities of such operations. In processing videos as a whole instead of images individually, video saliency detection approaches seek to apply temporal reasoning to improve the accuracy and consistency of saliency predictions, typically by propagating information from previous frames to be considered when processing future frames. By applying these concepts to the task of anomaly detection in UAV video the goal is to produce a general solution which is capable of detecting any object of interest in the video
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