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Cluster Computing
Published in: Cluster Computing 5/2019

09-01-2018

RETRACTED ARTICLE: The application of target tracking method in power load analysis based on computer vision in complex scenes

Authors: Dunnan Liu, Rui Ge, Qiang Ding, Mu li, Qi li, Mo Yang, D. Liu

Published in: Cluster Computing | Special Issue 5/2019

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Abstract

Considering the power load in the system has the characteristics of random variation, and the prediction of the power load is the key to ensure the safe operation of the power. At present, there are many methods of power load analysis, based on the computer video capture equipment and target tracking technology, the power load analysis has a good performance in the application. Therefore, in this paper, the algorithm of target tracking under complex scenes of power system operation was researched, its specific application in power load analysis was discussed, and then, the multi-target tracking, irregular target tracking and other technical problems of the target tracking algorithm in the application process in the real world were solved. According to taking the power load analysis of a park grid system as the practical application object, it proved that the research of the target tracking algorithm had a good application effect in the real power load analysis.
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Literature
1.
Ragaglia, M., Bascetta, L., Rocco, P.: Multiple camera human detection and tracking inside a robotic cell an approach based on image war, computer vision, k-d trees and particle filtering. Int. Conf. Inf. Control 59(11), 374–381 (2015)
2.
Xu, Y., Fang, G., Lv, N., Chen, S., Zou, J.J.: Computer vision technology for seam tracking in robotic GTAW and GMAW. Robot. Comput. Integr. Manuf. 32(6), 25–36 (2015)CrossRef
3.
Hohlfeld, O., Pomp, A., Link, J., Bitsch, G., Guse, D.: On the applicability of computer vision based gaze tracking in mobile scenarios. Mobilehci 17(10), 427–434 (2015)CrossRef
4.
Wang, J., Korayem, M., Blanco, S., Crandall, D.J.: Tracking natural events through social media and computer vision. ACM 23(8), 1097–1101 (2016)
5.
Ramachandran, M., Tyagi, A., Diaz Spindola, S.: Systems and methods of merging multiple maps for computer vision based tracking. FreePatentsOnline 18(2), 106–110 (2015)
6.
Zheng, Y., Xiao, S.: Performance analysis of a moving target tracking method based on computer vision. Eighth Int. Conf. Meas. Technol. Mechatron. Autom. 25(3), 467–470 (2016)
7.
Carretero, J., Blas, J.G.: Introduction to cloud computing: platforms and solutions. Cluster Comput. 17(4), 1225–1229 (2014)CrossRef
8.
Perski, H., Bitton, D.: System and method for computer vision based tracking of an object. Free Patents Online 9(1), 56–58 (2016)
9.
Schedulecontent, L., Schedule, E.: Tracking and detection in computer vision—winter term. Campar. In. Tum. De 35(6), 17–17 (2016)
10.
García, M.F.P., Issa, F.: System and method for computer vision item recognition and target tracking. Free Patents Online 10(9), 1045–1047 (2015)
11.
Zhou, Q.: Research on heterogeneous data integration model of group enterprise based on cluster computing. Cluster Comput. 19(3), 1–8 (2016)CrossRef
12.
Forkosh, E., Forkosh, M.: Computer vision system for tracking ball movement and analyzing user skill. Free Patents Online 22(12), 36–39 (2015)
13.
Zhang, P., Zhuo, T., Chen, K., Huang, W.: How does human interest modeling help in computer vision: tracking-by-saliency in unconstrained social videos. IEEE Int. Conf. Multimedia Expo Workshops 41(2), 1–6 (2016)
14.
Yang, H., Wang, Y., Gao, L.: A general line tracking algorithm based on computer vision. Control Decis. Conf. 27(3), 5365–5370 (2016)
15.
Rodriguez, A., Ricodiaz, A.J., Rabuñal, J.R., Gestal, M.: Fish tracking with computer vision techniques: an application to vertical slot fishways. IGI Glob. 108(1), 1–17 (2017)
16.
Leroux, M.: Assessment of accuracy for target detection in 3D-space using eye tracking and computer vision. Peer J. Preprint 101(11), 161–171 (2017)
17.
Kaur, D.P., Mantri, A.: Computer vision and sensor fusion for efficient hybrid tracking in augmented reality systems. IEEE Int. Conf. Moocs 6(6), 176–181 (2016)
18.
Mei, J., Li, K., Li, K.: Energy-aware task scheduling in heterogeneous computing environments. Cluster Comput. 17(2), 537–550 (2014)CrossRef
19.
Lintilä, T., Hyvärinen, V., Kariniemi, R., Kariniemi, R., Kc, A., Nurmi, L., et al.: Development of computer vision based face tracking measurement for sitting ergonomics. Aaltodoc. Aalto. Fi 23(6), 111–115 (2016)
Metadata
Title
RETRACTED ARTICLE: The application of target tracking method in power load analysis based on computer vision in complex scenes
Authors
Dunnan Liu
Rui Ge
Qiang Ding
Mu li
Qi li
Mo Yang
D. Liu
Publication date
09-01-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1405-3

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