Designing a Deep Neural Networks Structure to Acquire the Top Level Depiction of Human Interactions
S. Sandhya Rani1, G. Appa Rao Naidu2, V. Usha Shree3
1S. Sandhya Rani, Research Scholar, Department of CSE, JNTUH, India.
2Dr. G. Appa Rao Naidu, Professor, Department of CSE, JBIET, India.
3Dr. V. Usha Shree,Principal, Department of ECE, JBREC, India.
Manuscript received on October 01, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 1364-1368 | Volume-9 Issue-1, October 2019. | Retrieval Number: A1172109119/2019©BEIESP | DOI: 10.35940/ijeat.A1172.109119
Open Access | Ethics and Policies | Cite | Mendeley
© 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: The latest research studies have really concentrated on slim complications including individual action recognition approaches utilizing depth information, 3D-skeleton information, still photo applicable info, spatiotemporal interest point-based approaches, and also human strolling proposition recognition Nevertheless, there has actually really been no methodical research study of individual action recognition. We present the scene context features that describe the atmosphere of the topic worldwide and also local levels. We design a deep neural network structure to acquire the top-level depiction of human activity combining both activity attributes as well as context features.
Keywords: Deep learning, human recognition, context features.