Privacy-Preserving Human Activity Recognition and Resolution Image using Deep Learning Algorithms Spatial relationship and increasing the attribute value in OpenCV

  • K. Vijaya kumar , Dr.J.Harikiran, M. A. Rama Prasad, Uddagiri Sirisha ,

Abstract

Protecting privacy from hidden images and recorded videos is an important social issue. In order to store data related image or video information the system of human activity can be identified for analyzing the various issues of activities. But at the same time ensure that it does not record the important video or capture the actions of human it is violating the privacy of human. This article presents a fundamental approach to solving such conflicting tasks: recognition of human activity when using only anonymous low-resolution videos (Example: 18x16). We are introducing the Deep learning, CNN, Computer Vision OpenCV paradigm, the algorithm explaining the minimal image conversion and to create several low-resolution of highly resolution instructional videos from video. Our concepts is studying various types of sub-pixel transforms that are optimized for classifying activities, which allows the classroom specialist to make the best use of existing high-resolution videos by bringing several LR training videos adapted for this problem. The practical output results are confirmed that the OpenCV in computer models can benefit from activity recognition from extremely human activity videos. In this paper we are finding the minimal of human activities from the images or videos taken from the different public where the high resolution can be identified and getting the very low resolution and privacy can be hidden from such images or videos.

Published
2020-05-02
How to Cite
K. Vijaya kumar , Dr.J.Harikiran, M. A. Rama Prasad, Uddagiri Sirisha ,. (2020). Privacy-Preserving Human Activity Recognition and Resolution Image using Deep Learning Algorithms Spatial relationship and increasing the attribute value in OpenCV. International Journal of Advanced Science and Technology, 29(7), 514 - 523. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/13251
Section
Articles