Sensors, Vol. 19, Pages 4484: Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks

Sensors, Vol. 19, Pages 4484: Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks Sensors doi: 10.3390/s19204484 Authors: Víctor García Rubio Juan Antonio Rodrigo Ferrán Jose Manuel Menéndez García Nuria Sánchez Almodóvar José María Lalueza Mayordomo Federico Álvarez 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 method...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research