Fixed-wing aerial vehicles are equipped with functionalities such as ILS (instrument landing system), PAR (precision approach radar) and, DGPS (differential global positioning system), enabling fully automated landings. However, these systems impose significant costs on airport operations due to high installation and maintenance requirements. Moreover, since these navigation parameters come from ground or satellite signals, they are vulnerable to interference. A more cost-effective and independent alternative for guiding landing is a vision-based system that detects the runway and aligns the aircraft, reducing the pilot’s cognitive load. This paper proposes a novel framework that addresses three key challenges in developing autonomous vision-based landing systems. Firstly, to overcome the lack of aerial front-view video data, we created high-quality videos simulating landing approaches through the generator code available in the LARD (landing approach runway detection dataset) repository. Secondly, in contrast to former studies focusing on object detection for finding the runway, we chose the state-of-the-art model LoRAT to track runways within bounding boxes in each video frame. Thirdly, to align the aircraft with the designated landing runway, we extract runway keypoints from the resulting LoRAT frames and estimate the camera relative pose via the Perspective-n-Point algorithm. Our experimental results over a dataset of generated videos and original images from the LARD dataset consistently demonstrate the proposed framework’s highly accurate tracking and alignment capabilities. Our approach source code and the LoRAT model pre-trained with LARD videos are available at https:// github.com/ jpklock2/ visionbased-landing-guidance

This letter proposes an unsupervised approach for training deep semantic segmentation networks (DSSNs) with convolutional (CNN) layers: Weighted Dual SSN (WDSSN). Feature maps from the encoder-decoder block are grouped into subclusters before passing through the argmax layer. Pseudo labels for guiding the learning process are achieved with a superpixel refinement step. The network weight updates are computed through a batch-weighted cross-entropy (CE) loss proposed to tackle the problem of uneven class distributions usually present in remote sensing tasks. Experimental results show that our approach can learn high-level features from satellite imagery, and performs better than previously published unsupervised convolutional DSSN methods, obtaining accuracies of 63.3% in the Potsdam dataset, 76.3% in the Potsdam-3 dataset, and 39.0% in the Coco dataset. github.com/jpklock2/WDSSN.

Recent advances and applications of aerial image semantic segmentation have yielded an increase in their use in day-to-day tasks. However, state-of-art algorithms, composed mostly of deep semantic segmentation networks (DSSNs), may not be suitable for application domains in which labels (targeted output masks) are scarce. This work proposes a fully unsupervised semantic segmentation method able to find the appropriate number of semantics labels (yielded from clustering) without the need of a previously annotated dataset. Once the semantic labels are identified, a classifier trained from these labels can be used to assign semantics to new input images. In contrast to other clustering-based segmentation methods, our method does not require any input parameter to find the clustering partition that best accommodates data. Empirical results on two real datasets showed that the use of semantics in a template-matching-based unmanned aerial vehicle (UAV) localization pipeline reduced the overall position estimation error.

Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and shape modeling from images in the wild bring this challenge to the fore. Although the recent advances on view synthesis and image-to-image translation, currently available formulations are limited to transfer solely style and do not take into account the character's motion and shape, which are by nature intermingled to produce plausible human forms. In this paper, we propose a unifying formulation for transferring appearance and retargeting human motion from monocular videos that regards all these aspects. Our method synthesizes new videos of people in a different context where they were initially recorded. Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints. The evaluation is performed with several experiments using publicly available real videos containing hard conditions. Our method is able to transfer both human motion and appearance outperforming state-of-the-art methods, while preserving specific features of the motion that must be maintained (e.g., feet touching the floor, hands touching a particular object) and holding the best visual quality and appearance metrics such as Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).

Thanks to the advances in the technology of low-cost digital cameras and the popularity of the self-recording culture, the amount of visual data on the Internet is going to the opposite side of the available time and patience of the users. Thus, most of the uploaded videos are doomed to be forgotten and unwatched in a computer folder or website. In this work, we address the problem of creating smooth fast-forward videos without losing the relevant content. We present a new adaptive frame selection formulated as a weighted minimum reconstruction problem, which combined with a smoothing frame transition method accelerates first-person videos emphasizing the relevant segments and avoids visual discontinuities. The experiments show that our method is able to fast-forward videos to retain as much relevant information and smoothness as the state-of-the-art techniques in less time. We also present a new 80-hour multimodal (RGB-D, IMU, and GPS) dataset of first-person videos with annotations for recorder profile, frame scene, activities, interaction, and attention.