Self-Intelligence with Human Activities Recognition based on Convolutional Neural Networks 
R.Jayaraj1, Karishma Agarwal2, Utkarsh Singh3, Aparna Singh4
1R. Jayraj*, Assistant Professor (UG),Department of Information Technology, SRM Institute of Science and Technology, Ramapuram Campus, Chennai.
2Karishma Agarwal, Student, Department of Information Technology, SRM Institute of Science and Technology, Ramapuram Campus, Chennai.
3Aparna Singh, Student, Department of Information Technology, SRM Institute of Science and Technology, Ramapuram Campus, Chennai.
4Utkarsh Singh, Student, Department of Information Technology, SRM Institute of Science and Technology, Ramapuram Campus, Chennai.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 68-72 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6489049420/2020©BEIESP | DOI: 10.35940/ijeat.D6489.049420
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: In the presented paper, we propose a strategy related to activity recognition of human from profundity maps as well as sequences stance information using convolutional neural systems. Two information descriptors will be utilized for activity portrayal. The main information is a depth movement picture which will store back to back depth motion images of a human activity, whilst the subsequent data is the proposed moving joint description feature which conveys the movement of joints after time instants. To boost highlight extraction for precise activity arrangement, we will use three networked channels prepared with different inputs along with hypothesis verification. The activity results produced from those channels are intertwined for last activity characterization. Here, we suggest a few combination score based tasks to amplify the weightage of the correct activity. The experiments reveal the aftereffects of intertwining the yield of those channels along with the hypothesis are superior to utilizing a single channel or intertwining more than one channel in particular. The technique was assessed on two open databases which are Microsoft activity dataset and the second one is taken from University of Texas . The results demonstrate that our method beats the vast majority of existing cutting edge techniques, for example, histogram of arranged 4-D normal in datasets. Albeit DHA dataset has high number of activities (38 activities) contrasted with existing activity datasets, our paper outperforms a cutting edge strategy on the dataset by 6.9%.
Keywords: Convolutional neural networks (CNN), activity recognition, deep learning.