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Enriched recognition and monitoring algorithm for private cloud data centre

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Abstract

In the private cloud data center, security participated a fundamental position amid the storage of a voluminous amount of information that is intended to share among various nodes. On the other hand, the challenges in moving object detection and movement-based sub-sequences are significant segments of numerous PC apparition functions that incorporate acknowledgment of objects, assessment of interchange, and manufacturing mechanization. In this paper, we propose to actualize hearty moving object detection and following calculation that can recognize quick-paced moving objects in an assortment of testing constant quick-moving applications like traffic reconnaissance, etc. For the detection of moving objects, we utilize a Gaussian Mixture Design Background Subtraction Methodology. To remove noise, morphological processes are concerned with the resultant forefront pretence. Kalman Filter is utilized for movement-based monitoring and the detected object functions carry out movement segmentation using a foreground detector. Ultimately, blob analysis recognizes clusters of associated picture elements that are known to be moving objects, and the values are stored in a private cloud data center.

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Funding

This research work was fully supported by King Khalid University, Abha, Kingdom of Saudi Arabia, for funding this work through a Large Research Project under grant number RGP/161/42.

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Authors and Affiliations

  1. Department of Computer Science, Sarat Abida Campus-King Khalid University, Sarat Abida, Kingdom of Saudi Arabia

    R. Dhaya

  2. Department of Computer Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia

    R. Kanthavel

  3. Department of ECE, CMR College of Engineering and Technology, Hydrabad, India

    M. Mahalakshmi

Authors
  1. R. Dhaya
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  2. R. Kanthavel
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  3. M. Mahalakshmi
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Contributions

Conceptualization, RD; methodology, RD and RK; validation, MM; formal analysis, RK; investigation, RK; resources, MM; writing—original draft preparation, RD, and RK; supervision, RD; All authors have read and agreed to the published version of the manuscript.

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Correspondence to R. Dhaya.

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All authors declare that they have no conflicts of interest regarding the publication of this manuscript.

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Communicated by Joy Iong-Zong Chen.

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Dhaya, R., Kanthavel, R. & Mahalakshmi, M. Enriched recognition and monitoring algorithm for private cloud data centre. Soft Comput 26, 12871–12881 (2022). https://doi.org/10.1007/s00500-021-05967-z

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