Convolutional neural network for pixel-wise skyline detection

Outdoor augmented reality applications are an emerging class of software systems that demand the fast identification of natural objects, such as plant species or mountain peaks, in low power mobile devices. Convolutional Neural Networks (CNN) have exhibited superior performance in a variety of computer vision tasks, but their training is a labor intensive task and their execution requires non negligible memory and CPU resources. This paper presents the results of training a CNN for the fast extraction of mountain skylines, which exhibits a good balance between accuracy (94,45% in best conditions and 86,87% in worst conditions), memory consumption (9,36 MB on average) and runtime execution overhead (273 ms on a Nexus 6 mobile phone), and thus has been exploited for implementing a real-world augmented reality applications for mountain peak recognition running on low to mid-end mobile phones.