Enriched recognition and monitoring algorithm for private cloud data centre

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.

[1]  Richard Hill,et al.  Cloud-based scalable object detection and classification in video streams , 2018, Future Gener. Comput. Syst..

[2]  Scott A. Brandt,et al.  Visual tracking for intelligent vehicle-highway systems , 1996 .

[3]  Adam Kozakiewicz,et al.  Cloud Computing and Energy Efficiency: Mapping the Thematic Structure of Research , 2020, Energies.

[4]  Feifei Li,et al.  ATOM: Efficient Tracking, Monitoring, and Orchestration of Cloud Resources , 2017, IEEE Transactions on Parallel and Distributed Systems.

[5]  D. Boudreau,et al.  A fast automatic modulation recognition algorithm and its implementation in a spectrum monitoring application , 2000, MILCOM 2000 Proceedings. 21st Century Military Communications. Architectures and Technologies for Information Superiority (Cat. No.00CH37155).

[6]  Mona Saini,et al.  Survey On Vision Based On-Road Vehicle Detection , 2014 .

[7]  Hailong Zhu,et al.  Image data model optimization method based on cloud computing , 2020, Journal of Cloud Computing.

[8]  Pandian Vasant,et al.  Face recognition-based real-time system for surveillance , 2017, Intell. Decis. Technol..

[9]  Dawei Li,et al.  An energy-efficient algorithm for virtual machine placement optimization in cloud data centers , 2020, Cluster Computing.

[10]  Shu Gan,et al.  Application of Fractional Differential Calculation in Pretreatment of Saline Soil Hyperspectral Reflectance Data , 2018, J. Sensors.

[11]  Lisandro Zambenedetti Granville,et al.  Monitoring of cloud computing environments: concepts, solutions, trends, and future directions , 2016, SAC.

[12]  Naixue Xiong,et al.  Real-Time Cloud-Based Health Tracking and Monitoring System in Designed Boundary for Cardiology Patients , 2018, J. Sensors.

[13]  Branko Ristic,et al.  Bernoulli filter for joint detection and tracking of an extended object in clutter , 2013 .

[14]  Nick Antonopoulos,et al.  Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics , 2019, IEEE Transactions on Cloud Computing.

[15]  Yong Wang,et al.  A Cloud Based Object Recognition Platform for IOS , 2014, 2014 International Conference on Identification, Information and Knowledge in the Internet of Things.

[16]  Wei Song,et al.  A cloud-based monitoring system via face recognition using Gabor and CS-LBP features , 2016, The Journal of Supercomputing.

[17]  Brian C. Lovell,et al.  Object tracking via non-Euclidean geometry: A Grassmann approach , 2014, IEEE Winter Conference on Applications of Computer Vision.

[18]  Z. Zivkovic.  Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[19]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[20]  Chong Wang,et al.  New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Over Camera Network , 2015, IEEE Sensors Journal.

[21]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Fang Ming,et al.  An improved face recognition algorithm and its application in attendance management system , 2020, Array.

[24]  Lidong Chen,et al.  An approach for fast and parallel video processing on Apache Hadoop clusters , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).