Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos

Abstract

Various studies have been conducted for detecting humans in images. However, there are the cases where a part of human body disappears in the input image and leaves the camera field of view (FOV). Moreover, there are the cases where a pedestrian comes into the FOV as a part of the body slowly appears. In these cases, human detection and tracking fail by existing methods. Therefore, we propose the method for predicting a wider region than the FOV of a thermal camera based on the image prediction generative adversarial network version 2 (IPGAN-2). When an experiment was conducted using the marathon subdataset of the Boston University-thermal infrared video benchmark open dataset, the proposed method showed higher image prediction (structural similarity index measure (SSIM) of 0.9437) and object detection (F1 score of 0.866, accuracy of 0.914, and intersection over union (IoU) of 0.730) accuracies than state-of-the-art methods.