Related papers: Sifting Through the Static: Moving Object Detectio…
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments. Separating moving objects from static ones is essential for navigation, pose estimation, and understanding how other traffic participants are likely to…
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models often require large datasets labeled with bounding box…
Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical…
The NASA Orbital Debris Observatory (NODO) astronomical survey uses a transit 3-m liquid mirror telescope to observe a strip of sky in 20 narrow-band filters. In this article, we analyze a subset of data from the 1996 observing season. The…
Detection of moving objects is an essential capability in dealing with dynamic environments. Most moving object detection algorithms have been designed for color images without depth. For robotic navigation where real-time RGB-D data is…
We present a catalog of 316 trans-Neptunian bodies detected by the Dark Energy Survey (DES). These objects include 245 discoveries by DES (139 not previously published) detected in $\approx 60,000$ exposures from the first four seasons of…
We present our latest results about the short-term variability of trans-Neptunian objects (TNOs). We performed broad-band CCD photometric observations using several telescopes in Spain and Chile. We present results based on three years of…
Thousands of small bodies, known as trans-Neptunian objects (TNOs), orbit the Sun beyond Neptune. TNOs are remnants of the planets' formation from a disc of gas and dust, so it is puzzling that they move mostly on eccentric orbits inclined…
Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting…
We present the results of a wide-field survey designed to measure the size, inclination, and radial distributions of Kuiper Belt Objects (KBOs). The survey found 86 KBOs in 73 square degrees observed to limiting red magnitude 23.7 using the…
Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on-board camera. Recent advances in deep learning have made it possible to use…
Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are…
Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses…
We have been conducting a survey for distant solar system objects beyond the Kuiper Belt edge (~50 AU) with new wide-field cameras on the Subaru 8 meter and CTIO 4 meter telescopes. We are interested in the orbits of objects that are…
In the past two decades an increasing interest in discovering Near Earth Objects has been noted in the astronomical community. Dedicated surveys have been operated for data acquisition and processing, resulting in the present discovery of…
Transient, star-like point sources that appear and vanish over short timescales are described in astronomical images prior to launch of Sputnik. We have reported that transient numbers diminish significantly in Earth's shadow (shadow…
The recently postulated existence of a giant ninth planet in our solar system has sparked search efforts for distant solar system objects (SSOs) both via new observations and archival data analysis. Due to the likely faintness of the object…
This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. Our model consists of three parts, the backbone, the neck, and the prediction head. The…
Moving object detection (MOD) in remote sensing is significantly challenged by low resolution, extremely small object sizes, and complex noise interference. Current deep learning-based MOD methods rely on probability density estimation,…
Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions…