scholarly journals Real-Time Web Map Construction Based on Multiple Cameras and GIS

2021 ◽  
Vol 10 (12) ◽  
pp. 803
Author(s):  
Xingguo Zhang ◽  
Xinyu Shi ◽  
Xiaoyue Luo ◽  
Yinping Sun ◽  
Yingdi Zhou

Previous VideoGIS integration methods mostly used geographic homography mapping. However, the related processing techniques were mainly for independent cameras and the software architecture was C/S, resulting in large deviations in geographic video mapping for small scenes, a lack of multi-camera video fusion, and difficulty in accessing real-time information with WebGIS. Therefore, we propose real-time web map construction based on the object height and camera posture (RTWM-HP for short). We first consider the constraint of having a similar height for each object by constructing an auxiliary plane and establishing a high-precision homography matrix (HP-HM) between the plane and the map; thus, the accuracy of geographic video mapping can be improved. Then, we map the objects in the multi-camera video with overlapping areas to geographic space and perform the object selection with the multi-camera (OS-CDD) algorithm, which includes the confidence of the object, the distance, and the angle between the objects and the center of the cameras. Further, we use the WebSocket technology to design a hybrid C/S and B/S software framework that is suitable for WebGIS integration. Experiments were carried out based on multi-camera videos and high-precision geospatial data in an office and a parking lot. The case study’s results show the following: (1) The HP-HM method can achieve the high-precision geographic mapping of objects (such as human heads and cars) with multiple cameras; (2) the OS-CDD algorithm can optimize and adjust the positions of the objects in the overlapping area and achieve a better map visualization effect; (3) RTWM-HP can publish real-time maps of objects with multiple cameras, which can be browsed in real time through point layers and hot-spot layers through WebGIS. The methods can be applied to some fields, such as person or car supervision and the flow analysis of customers or traffic passengers.

2020 ◽  
Vol 14 (02) ◽  
pp. 1
Author(s):  
Guofeng Tong ◽  
Yong Li ◽  
Yuanyuan Li ◽  
Fan Gao ◽  
Lihao Cao

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61570-61580 ◽  
Author(s):  
Weichen Li ◽  
Junying Xia ◽  
Ge Zhang ◽  
Hang Ma ◽  
Benyuan Liu ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.


2021 ◽  
Vol 6 (2) ◽  
pp. 421-428
Author(s):  
Matteo Palieri ◽  
Benjamin Morrell ◽  
Abhishek Thakur ◽  
Kamak Ebadi ◽  
Jeremy Nash ◽  
...  
Keyword(s):  

Author(s):  
Tingting Yin ◽  
Zhong Yang ◽  
Youlong Wu ◽  
Fangxiu Jia

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.


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