A Smart Home Monitoring System for Abnormal Human Activity Detection using CNN
Samreen Sultana1, M.Narayana2

1Samreen Sultana*, pursuing M.Tech Digital Electronics and Communication Systems, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India.
2Dr.M.Narayana,Ph.D, Professor, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3421-3426 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25871081219/2019©BEIESP | DOI: 10.35940/ijitee.L2587.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Demonstrating human practices and movement designs for acknowledgment or location of exceptional occasion has pulled in noteworthy research enthusiasm for late years. Differing strategies that are flourish for structure smart vision frameworks went for scene comprehension and making right semantic derivation from the watched elements of moving targets. Most applications are in reconnaissance, video content recovery, and human PC interfaces. In this propose a novel strategy for irregular human action recognition in jam-packed scenes/Home. In particular, as opposed to recognizing or fragmenting people, we formulated a productive technique, called a movement impact map, for speaking to human exercises. The key element of the proposed movement impact guide is that it viably mirrors the movement qualities of the development speed, development bearing, and size of the items or subjects and their communications inside an edge succession. In this propose System developing using CNN.
Keywords: Un-usual Motion Recognition, Visualization-Base Surveillance, Action Control Plot, and full Scene, Home.
Scope of the Article: Pattern Recognition