A spatio-temporal semantic model based on spatial information multi-grid

Author(s):  
Huiqiong Xia ◽  
Deren Li ◽  
Zhenfeng Shao
2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 126965-126976
Author(s):  
Xiaoyu Cong ◽  
Yubing Han ◽  
Weixing Sheng ◽  
Shanhong Guo ◽  
Renli Zhang

Author(s):  
XIAN WU ◽  
JIANHUANG LAI ◽  
PONG C. YUEN

This paper proposes a novel approach for video-shot transition detection using spatio-temporal saliency. Both temporal and spatial information are combined to generate a saliency map, and features are available based on the change of saliency. Considering the context of shot changes, a statistical detector is constructed to determine all types of shot transitions by the minimization of the detection-error probability simultaneously under the same framework. The evaluation performed on videos of various content types demonstrates that the proposed approach outperforms a more recent method and two publicly available systems, namely VideoAnnex and VCM.


2021 ◽  
pp. 108453
Author(s):  
Huakang Li ◽  
Yidan Qiu ◽  
Huimin Zhao ◽  
Jin Zhan ◽  
Rongjun Chen ◽  
...  

2001 ◽  
Vol 10 (04) ◽  
pp. 715-734 ◽  
Author(s):  
SHU-CHING CHEN ◽  
MEI-LING SHYU ◽  
CHENGCUI ZHANG ◽  
R. L. KASHYAP

The identification of the overlapped objects is a great challenge in object tracking and video data indexing. For this purpose, a backtrack-chain-updation split algorithm is proposed to assist an unsupervised video segmentation method called the "simultaneous partition and class parameter estimation" (SPCPE) algorithm to identify the overlapped objects in the video sequence. The backtrack-chain-updation split algorithm can identify the split segment (object) and use the information in the current frame to update the previous frames in a backtrack-chain manner. The split algorithm provides more accurate temporal and spatial information of the semantic objects so that the semantic objects can be indexed and modeled by multimedia input strings and the multimedia augmented transition network (MATN) model. The MATN model is based on the ATN model that has been used in artificial intelligence (AI) areas for natural language understanding systems, and its inputs are modeled by the multimedia input strings. In this paper, we will show that the SPCPE algorithm together with the backtrack-chain-updation split algorithm can significantly enhance the efficiency of spatio-temporal video indexing by improving the accuracy of multimedia database queries related to semantic objects.


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