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.
Highlights
Extensive research has been conducted on objection detection [1,2,3,4], tracking [5,6,7,8,9], and action recognition [10,11,12,13] using conventional camera-based detection systems
The issue in which a part of human body disappears was examined [14], but only a small region within an input image could be predicted. To overcome such an issue, in this study, for the first time, an image restoration was performed, as shown in Figure 1, by predicting the wide region outside the field of view (FOV) not included in the current image (t) as in image t’ for restoring the disappeared part of the body of a pedestrian in a thermal image
The regions outside the FOV were predicted using the image obtained from a thermal camera that measured the heat of a human body rather than the image obtained from a general visible light camera
Summary
Extensive research has been conducted on objection detection [1,2,3,4], tracking [5,6,7,8,9], and action recognition [10,11,12,13] using conventional camera-based detection systems. The issue in which a part of human body disappears was examined [14], but only a small region within an input image could be predicted. To overcome such an issue, in this study, for the first time, an image restoration was performed, as shown, by predicting the wide region outside the FOV not included in the current image (t) as in image t’ for restoring the disappeared part of the body of a pedestrian in a thermal image.
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