scholarly journals Pose Estimation of GIS Pipeline Based on Spatial Transformation Network

2021 ◽  
Vol 2066 (1) ◽  
pp. 012021
Author(s):  
Gong Zhang ◽  
Boming Li ◽  
Ti Liu ◽  
Wenhan Chen ◽  
Yichang Wang
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Structure Alignment ◽  
Spatial Transformation ◽  
High Similarity ◽  
Multiple Points ◽  
Key Points ◽  
Instrument Measurement ◽  
Pixel Mapping ◽  
Relationship Of

Abstract The manual flange structure alignment between GIS pipelines in the power system is inefficient and difficult to accurately align. To solve this problem, combined with the research results in the field of deep learning named spatial transformation network, a new pose estimation method based on single camera is proposed. In view of the high similarity between the moving flange and the static flange at the pixel level, the spatial transformation network is used to find the pixel mapping relationship of the two flange images. Thereby establishing the mapping relationship between the pixel coordinates of the two flange images and then using multiple points. In the perspective method, the pixel coordinates are mapped to the world coordinates to obtain the estimation of the position of the key point in the flange, and then the direction vector of the flange is calculated according to the position of the key point. Since there is a pixel coordinate transformation relationship between the static flange and the movable flange. Only the position of the key point in the static flange can be inversely solved by measuring the position of the key point in the static flange. Experiments show that, compared to the traditional method of measuring flange pose based on instrument measurement and linear regression, the method proposed in this paper can accurately measure the pose of the flange structure. And it can rely as little as possible on the measurement of the key points of the moving flange by the instrument.

scholarly journals Privacy-Oriented Successive Approximation Image Position Follower Processing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ying Miao ◽  
Danyang Shao ◽  
Zhimin Yan
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Successive Approximation ◽  
Pose Estimation ◽  
Human Body ◽  
Estimation Method ◽  
Correlation Filter ◽  
Human Body Model ◽  
Dynamic Background ◽  
Key Points

In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.

scholarly journals The Aircraft Pose Estimation Based on a Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Daoyong Fu ◽  
Wei Li ◽  
Songchen Han ◽  
Xinyan Zhang ◽  
Zhaohuan Zhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Estimation Method ◽  
Human Pose Estimation ◽  
Two Dimensional ◽  
Surveillance Video ◽  
Key Points ◽  
Human Pose

The pose estimation of the aircraft in the airport plays an important role in preventing collisions and constructing the real-time scene of the airport. However, current airport target surveillance methods regard the aircraft as a point, neglecting the importance of pose estimation. Inspired by human pose estimation, this paper presents an aircraft pose estimation method based on a convolutional neural network through reconstructing the two-dimensional skeleton of an aircraft. Firstly, the key points of an aircraft and the matching relationship are defined to design a 2D skeleton of an aircraft. Secondly, a convolutional neural network is designed to predict all key points and components of the aircraft kept in the confidence maps and the Correlation Fields, respectively. Thirdly, all key points are coarsely matched based on the matching relationship and then refined through the Correlation Fields. Finally, the 2D skeleton of an aircraft is reconstructed. To overcome the lack of benchmark dataset, the airport surveillance video and Autodesk 3ds Max are utilized to build two datasets. Experiment results show that the proposed method get better performance in terms of accuracy and efficiency compared with other related methods.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

RATIONALE FOR MONITORING GROUNDWATER LEVELS IN THE REHABILITATED PEATLANDS OF PRIPYAT POLESIE

2020 ◽  
pp. 33-43
Author(s):  
Olga N. Ratnicava ◽  
Irina P. Lisitsyna ◽  
Inna V. Аgeichik
Keyword(s):  
Field Studies ◽  
Natural State ◽  
Groundwater Levels ◽  
Discharge Water ◽  
Peat Deposits ◽  
Disturbed Areas ◽  
Key Points ◽  
Before And After ◽  
Vegetation Maps ◽  
Relationship Of

Based on studies of geomorphology, stratigraphy, hydrology, various maps of Polesie, zones of influence of amelioration canals, vegetation maps, modern satellite images, as well as field studies of peatlands of Pripyat Polesie, two independent drainage systems have been identified, with a network of amelioration canals that intensively discharge water into the rivers Stwiga and Ybort`. Maps of key points were built In GIS-format, on which five sites were laid in the field within the Mezhch and Neresnya peat deposits for further long-term monitoring of GWL parameters. The locations of the sensors installation are based on the relationship of bog phytocenoses with the average annual GWL values and the amplitude of their fluctuations. Analysis of the GWL parameters before and after environmental rehabilitation measures will allow assessing the effectiveness of planned measures in disturbed areas and obtaining new data on areas of peat deposits in their natural state.

Non-iterative pose estimation method based on the polarization information via a parallelogram

Measurement ◽  
2022 ◽  
Vol 187 ◽  
pp. 110274
Author(s):  
Zhang Zimiao ◽  
Xu kai ◽  
Wu Yanan ◽  
Zhang Shihai
Keyword(s):  
Pose Estimation ◽  
Estimation Method

Narrating The City: spaces Of Urban Change, South London

2008 ◽  
Vol 33 (2) ◽  
pp. 10-17
Author(s):  
Suzanne M. Hall
Keyword(s):  
Inner City ◽  
The Other ◽  
Urban Change ◽  
Spatial Transformation ◽  
Class Culture ◽  
Relationship Of ◽  
The City ◽  
North And South ◽  
Official Data ◽  
Over Time

This paper explores the documentation of social and spatial transformation in the Walworth area, South London. Spatial narratives are the entry point for my exploration, where official and ‘unofficial’ representations of history are aligned to capture the nature of urban change. Looking at the city from street level provides a worldly view of social encounter and spaces that are expressive of how citizens experience and shape the city. A more distanced view of the city accessed from official data reveals different constructs. In overlaying near and far views and data and experience, correlations and contestations emerge. As a method of research, the narrative is the potential palimpsest, incorporating fragments of the immediate and historic without representing a comprehensive whole. In this paper Walworth is documented as a local and Inner City context where remnants and insertions are juxtaposed, where white working class culture and diverse ethnicities experience difference and change. A primary aim is to consider the diverse experiences of groups and individuals over time, through their relationship with their street, neighbourhood and city. In relating the Walworth area to London I use three spatial narratives to articulate the contemporary and historic relationship of people to place: the other side examines the physical discrimination between north and south London, the other half looks at distinctions of class and race and other histories explores the histories displaced from official accounts.

Accurate O(n) pose estimation method using both lines and points

Optik ◽  
2016 ◽  
Vol 127 (19) ◽  
pp. 7875-7880
Author(s):  
Meng Li ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Changyuan Wang
Keyword(s):  
Pose Estimation ◽  
Estimation Method
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