Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks

Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks

In recent years, the use of unmanned aerial vehicles (UAVs) for surveillance tasks has increased considerably. This technology provides a versatile and innovative approach to the field. However, the automation of tasks such as object recognition or change detection usually requires image processing...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 19; no. 20; p. 4484
Main Authors: García Rubio, Víctor, Rodrigo Ferrán, Juan Antonio, Menéndez García, Jose Manuel, Sánchez Almodóvar, Nuria, Lalueza Mayordomo, José María, Álvarez, Federico
Format: Journal Article
Language:English
Published: Basel MDPI AG 16-10-2019
MDPI
Subjects:
Algorithms
Automation
Cameras
Change detection
convolutional neural networks
Flight conditions
Human error
Image acquisition
image alignment
Image analysis
Image processing
Methods
moving camera
Neural networks
Object motion
Sequences
Surveillance
uav
Unmanned aerial vehicles
Weather
Wind effects
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Summary:In recent years, the use of unmanned aerial vehicles (UAVs) for surveillance tasks has increased considerably. This technology provides a versatile and innovative approach to the field. However, the automation of tasks such as object recognition or change detection usually requires image processing techniques. In this paper we present a system for change detection in video sequences acquired by moving cameras. It is based on the combination of image alignment techniques with a deep learning model based on convolutional neural networks (CNNs). This approach covers two important topics. Firstly, the capability of our system to be adaptable to variations in the UAV flight. In particular, the difference of height between flights, and a slight modification of the camera’s position or movement of the UAV because of natural conditions such as the effect of wind. These modifications can be produced by multiple factors, such as weather conditions, security requirements or human errors. Secondly, the precision of our model to detect changes in diverse environments, which has been compared with state-of-the-art methods in change detection. This has been measured using the Change Detection 2014 dataset, which provides a selection of labelled images from different scenarios for training change detection algorithms. We have used images from dynamic background, intermittent object motion and bad weather sections. These sections have been selected to test our algorithm’s robustness to changes in the background, as in real flight conditions. Our system provides a precise solution for these scenarios, as the mean F-measure score from the image analysis surpasses 97%, and a significant precision in the intermittent object motion category, where the score is above 99%.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19204484