aCN-RB-tree: Update Method for Spatio-Temporal Aggregation of Moving Object Trajectory in Ubiquitous Environment

2009 ◽  
Author(s):  
Dong-Wook Lee ◽  
Sung-Ha Baek ◽  
Hae-Young Bae
Keyword(s):  
Moving Object ◽  
Temporal Aggregation ◽  
Ubiquitous Environment ◽  
Spatio Temporal ◽  
Moving Object Trajectory

scholarly journals Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Changjun Wan ◽  
Changxiu Cheng ◽  
Sijing Ye ◽  
Shi Shen ◽  
Ting Zhang
Keyword(s):  
Spatial Autocorrelation ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Temporal Aggregation ◽  
Global Changes ◽  
Percentile Method ◽  
Extreme Precipitation Events ◽  
Spatio Temporal ◽  
Precipitation Events ◽  
Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

Enabling spatio-temporal aggregation in Birds-Eye-View Vehicle Estimation

2021 ◽  
Author(s):  
Avishkar Saha ◽  
Oscar Mendez ◽  
Chris Russell ◽  
Richard Bowden
Keyword(s):  
Temporal Aggregation ◽  
Spatio Temporal

Power Supply Scheduling Optimization from a Viewpoint of Spatio-Temporal Aggregation

2014 ◽  
Vol 47 (3) ◽  
pp. 4571-4577
Author(s):  
Tomonori Sadamoto ◽  
Ikuma Muto ◽  
Takayuki Ishizaki ◽  
Masakazu Koike ◽  
Jun-ichi Imura
Keyword(s):  
Power Supply ◽  
Temporal Aggregation ◽  
Scheduling Optimization ◽  
Spatio Temporal

scholarly journals PIXEL-BY-PIXEL ESTIMATION OF SCENE MOTION IN VIDEO

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 61-65 ◽  
Author(s):  
A. G. Tashlinskii ◽  
P. V. Smirnov ◽  
M. G. Tsaryov
Keyword(s):  
Recursive Estimation ◽  
Stochastic Gradient ◽  
Moving Object ◽  
Stochastic Gradient Descent ◽  
Processing Efficiency ◽  
Estimated Parameters ◽  
Gradient Descent Algorithm ◽  
The Right ◽  
Moving Object Trajectory ◽  
Inter Frame

The paper considers the effectiveness of motion estimation in video using pixel-by-pixel recurrent algorithms. The algorithms use stochastic gradient decent to find inter-frame shifts of all pixels of a frame. These vectors form shift vectors’ field. As estimated parameters of the vectors the paper studies their projections and polar parameters. It considers two methods for estimating shift vectors’ field. The first method uses stochastic gradient descent algorithm to sequentially process all nodes of the image row-by-row. It processes each row bidirectionally i.e. from the left to the right and from the right to the left. Subsequent joint processing of the results allows compensating inertia of the recursive estimation. The second method uses correlation between rows to increase processing efficiency. It processes rows one after the other with the change in direction after each row and uses obtained values to form resulting estimate. The paper studies two criteria of its formation: gradient estimation minimum and correlation coefficient maximum. The paper gives examples of experimental results of pixel-by-pixel estimation for a video with a moving object and estimation of a moving object trajectory using shift vectors’ field.

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