Detection and tracking of moving cloud services from video using saliency map model

In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency ma...

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Bibliographic Details
Published inJournal of discrete mathematical sciences & cryptography Vol. 25; no. 4; pp. 1083 - 1092
Main Authors Kamble, Shailesh, Saini, Dilip Kumar J., Kumar, Vinay, Gautam, Arun Kumar, Verma, Shikha, Tiwari, Ashish, Goyal, Dinesh
Format Journal Article
LanguageEnglish
Published Taylor & Francis 19.05.2022
Subjects
Extended kalman filter
Object detection
Object tracking
Primary 93A30
Saliency map
Secondary 49K15
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Summary:In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency map model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.
ISSN:0972-0529
2169-0065
DOI:10.1080/09720529.2022.2072436