Recent Advances in Camera Planning for Large Area Surveillance

2016 ◽  
Vol 49 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Junbin Liu ◽  
Sridha Sridharan ◽  
Clinton Fookes
Keyword(s):  
Large Area ◽  
Recent Advances ◽  
Camera Planning ◽  
Area Surveillance

scholarly journals Recent Advances in Remote Sensing of Evapotranspiration

2021 ◽  
Vol 13 (21) ◽  
pp. 4260
Author(s):  
Nishan Bhattarai ◽  
Pradeep Wagle
Keyword(s):  
Remote Sensing ◽  
Environmental Sustainability ◽  
Global Environmental Change ◽  
Global Scale ◽  
Ecosystem Functions ◽  
Special Issue ◽  
Large Area ◽  
Modeling Tools ◽  
Recent Advances ◽  
Wide Range

Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products.

Camera planning for area surveillance: A new method for coverage inference and optimization using Location-based Service data

2019 ◽  
Vol 78 ◽  
pp. 101396 ◽  
Author(s):  
Zhigang Han ◽  
Songnian Li ◽  
Caihui Cui ◽  
Hongquan Song ◽  
Yunfeng Kong ◽  
...  
Keyword(s):  
New Method ◽  
Location Based Service ◽  
Camera Planning ◽  
Area Surveillance ◽  
Service Data

Recent advances in phosphorescent OLEDs for small- and large-area-display sizes

2006 ◽  
Vol 14 (5) ◽  
pp. 449 ◽  
Author(s):  
M. S. Weaver ◽  
R. C. Kwong ◽  
V. A. Adamovich ◽  
M. Hack ◽  
J. J. Brown
Keyword(s):  
Large Area ◽  
Recent Advances

Cooperative Large Area Surveillance with a Team of Aerial Mobile Robots for Long Endurance Missions

2012 ◽  
Vol 70 (1-4) ◽  
pp. 329-345 ◽  
Author(s):  
Jose Joaquin Acevedo ◽  
Begoña C. Arrue ◽  
Ivan Maza ◽  
Anibal Ollero
Keyword(s):  
Mobile Robots ◽  
Large Area ◽  
Area Surveillance ◽  
Long Endurance

Mapping the Nation's Physiography by Computer

1990 ◽  
pp. 15-24 ◽  
Author(s):  
Richard J. Pike ◽  
Gail P. Thelin
Keyword(s):  
United States ◽  
Computer Technology ◽  
Digital Image ◽  
Satellite Images ◽  
Grid Resolution ◽  
Large Area ◽  
Proprietary Software ◽  
Recent Advances ◽  
Shaded Relief ◽  
Photometric Function

Recent advances in computer technology present opportunities for the machine visualization of topography. A new shaded relief map of the conterminous United States is the first one-sheet graphic of U.S. landforms larger than Erwin Raisz's classic 1939 hand-drawn panorama. The 1:3,500,000-scale digital image (about 4.5' long), reproduced here at 1:10,000,000, has greater fidelity and detail than portrayals of this large area by artistic (manual) techniques. The new map also shows synoptictopography more clearly than contoured elevations, satellite images, or radar mosaics. We created the map by processing 12,000,000 elevations (digitized from 1:250,000-scale topographic sheets at a grid resolution of 0.8 km) on a V AX-11/780 computer, using proprietary software, a modified Lambert photometric function, 255 gray tones, and the method of Pinhas Yoeli as implemented by Raymond Batson and others.

Recent advances with mercuric iodide x-ray detectors and large-area silicon avalanche photodiodes

1993 ◽  
Author(s):  
Andrzej J. Dabrowski ◽  
Jan S. Iwanczyk ◽  
Yuzhong J. Wang ◽  
Michael C. Madden ◽  
Marek Szawlowski
Keyword(s):  
Avalanche Photodiodes ◽  
Mercuric Iodide ◽  
Large Area ◽  
X Ray ◽  
Recent Advances

Recent Advances in Large Area Avalanche Photodiodes

1985 ◽  
Vol 32 (1) ◽  
pp. 563-566 ◽  
Author(s):  
M. R. Squillante ◽  
G. Reiff ◽  
G. Entine
Keyword(s):  
Avalanche Photodiodes ◽  
Large Area ◽  
Recent Advances

scholarly journals Recent advances in high-efficiency organic solar cells fabricated by eco-compatible solvents at relatively large-area scale

APL Materials ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 120901
Author(s):  
Ling Hong ◽  
Huifeng Yao ◽  
Yong Cui ◽  
Ziyi Ge ◽  
Jianhui Hou
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
High Efficiency ◽  
Large Area ◽  
Recent Advances

Recent advances in very large area avalanche photodiodes

2003 ◽  
Author(s):  
Michael R. Squillante ◽  
James Christian ◽  
Gerald Entine ◽  
Richard Farrell ◽  
Arieh M. Karger ◽  
...  
Keyword(s):  
Avalanche Photodiodes ◽  
Large Area ◽  
Recent Advances

scholarly journals An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance

2018 ◽  
Vol 8 (11) ◽  
pp. 2222 ◽  
Author(s):  
Chengbin Peng ◽  
Wei Bu ◽  
Jiangjian Xiao ◽  
Ka-chun Wong ◽  
Minmin Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Public Safety ◽  
Low Resolution ◽  
Large Area ◽  
Large Margin ◽  
The Face ◽  
Cascade Algorithms ◽  
Area Surveillance

Face detection for security cameras monitoring large and crowded areas is very important for public safety. However, it is much more difficult than traditional face detection tasks. One reason is, in large areas like squares, stations and stadiums, faces captured by cameras are usually at a low resolution and thus miss many facial details. In this paper, we improve popular cascade algorithms by proposing a novel multi-resolution framework that utilizes parallel convolutional neural network cascades for detecting faces in large scene. This framework utilizes the face and head-with-shoulder information together to deal with the large area surveillance images. Comparing with popular cascade algorithms, our method outperforms them by a large margin.

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