Abstract
In the process of monitoring the gymnasium by the traditional radio frequency technology, the parallel computing problem of the large data environment in the gymnasium monitoring cannot be handled effectively. It cannot be identified independently and accurately, and the gymnasium monitoring algorithm based on large data is proposed. In the process of Map-Reduce parallel monitoring based on AE, off-line training of monitoring image recognition model based on AE and neural network is carried out. Through the weighted fusion algorithm of trajectory correction, the best data fusion result is obtained, and the offline training recognition model is used to identify the image information. In parallel monitoring, if there is a correlation between the monitoring events, the Map function is used to read the test sample data, and the mapping of the corresponding key values is obtained. The mapping records generated by the Map function are performed by the Reduce function to obtain the monitoring and identification results of the gymnasium. The experimental results show that the proposed algorithm can accurately and efficiently identify the monitoring images of the gymnasium.
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13 September 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11042-022-13869-x
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Acknowledgements
This work was supported by Guangdong Provincial Key Laboratory of Technology and Finance & Big Data Analysis (Grant No.2017B030301010); Platform of Credit Financing and Trade for Guangdong Technological Enterprises (Grant No.2014B080807035); Construction of New Technology Credit Service Platform Based on O2O Mode (Grant No.2017B080802004); Guangdong Key Research Base of Technology and Finance (Grant No.2014B030303005); Guangdong Technology & Finance Information Service Platform (Grant No.2015B080807015).
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Han, K., Xiao, G. & Yang, X. RETRACTED ARTICLE: An effective stadium monitoring control algorithm based on big data in emerging mobile networks. Multimed Tools Appl 78, 29971–29987 (2019). https://doi.org/10.1007/s11042-018-6688-8
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DOI: https://doi.org/10.1007/s11042-018-6688-8