An efficient method is proposed for refining GPS-acquired location coordinates in urban areas using camera images, Google Street View (GSV) and sensor parameters. The main goal is to compensate for GPS location imprecision in dense area of cities due to proximity to walls and buildings. Avail-able methods for better localization often use visual information by using query images acquired with camera-equipped mobile devices and applying image retrieval techniques to find the closest match in a GPS-referenced image data set. The search areas required for reliable search are about 1-2 sq. Km and the accuracy is typically 25-100 meters. Here we describe a method based on image retrieval where a reliable search can be confined to areas of 0.01 sq. Km and the accuracy in our experiments is less than 10 meters. To test our procedure we created a database by acquiring all Google Street View images close to what is seen by a pedestrian in a large region of downtown Chicago and saved all coordinates and orientation data to be used for confining our search region. Prior knowledge from approximate position of query image is leveraged to address complexity and accuracy issues of our search in a large scale geo-tagged data set. One key aspect that differentiates our work is that it utilizes the sensor information of GPS SOS and the camera orientation in improving localization. Finally we demonstrate retrieval-based technique are less accurate in sparse open areas compared with purely GPS measurement. The effectiveness of our approach is discussed in detail and experimental results show improved performance when compared with regular approaches.
CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras
University of Macau（澳门大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Juntendo University（顺天堂大学）；Peking University（北京大学）
Autonomous navigation for low-altitude UAVs in urban areas
Survey Copter / Airbus Defense and Space（空客防务与航天公司Survey Copter部门）；Hubert Curien Laboratory（于贝尔·居里安实验室）；Center for Research in Computer Vision, UCF（中佛罗里达大学计算机视觉研究中心）
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Northwestern Polytechnical University（西北工业大学）；The Hong Kong University of Science and Technology（香港科技大学）
