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Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video

Gökstorp, S. and Breckon, T.P. (2021) 'Temporal and Non-Temporal Contextual Saliency Analysis for Generalized Wide-Area Search within Unmanned Aerial Vehicle (UAV) Video.', The Visual Computer .

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 using a visual saliency approach to automatically analyse and detect anomalies in UAV video. Our Temporal Contextual Saliency (TeCS) model is based on the state-ofthe-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 LSTM layer and modifications to the base model. We additionally evaluate the impact of temporal vs nontemporal 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.

Item Type:Article
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
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Available under License - Creative Commons Attribution 4.0.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/s00371-021-02264-6
Publisher statement:This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Date accepted:14 July 2021
Date deposited:24 August 2021
Date of first online publication:06 September 2021
Date first made open access:18 November 2021

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