This paper is published in Volume-5, Issue-3, 2019
Area
Computer Science Engineering
Author
K. C. Mithil Teja
Co-authors
Sharmila Agnil, R. Bhargava Ramakrishna, T. Tharunkumar Reddy, A. Harsha Kiran
Org/Univ
SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
Pub. Date
03 May, 2019
Paper ID
V5I3-1159
Publisher
Keywords
Input video, Video Frame Separation, Frame image sequence, Background separation, Noise removal, Shape analysis

Citationsacebook

IEEE
K. C. Mithil Teja, Sharmila Agnil, R. Bhargava Ramakrishna, T. Tharunkumar Reddy, A. Harsha Kiran. Data efficient approaches on deep action recognition in videos, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
K. C. Mithil Teja, Sharmila Agnil, R. Bhargava Ramakrishna, T. Tharunkumar Reddy, A. Harsha Kiran (2019). Data efficient approaches on deep action recognition in videos. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.

MLA
K. C. Mithil Teja, Sharmila Agnil, R. Bhargava Ramakrishna, T. Tharunkumar Reddy, A. Harsha Kiran. "Data efficient approaches on deep action recognition in videos." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.

Abstract

This Method goes for one recently bringing assignment up in a vision and mixed media look into perceiving human activities from still pictures. Its principal challenges lie in the substantial varieties in human stances and appearances, just as the absence of worldly movement data. Tending to these issues, we propose to build up an expressive profound model to normally incorporate human format and encompassing settings for more elevated amount activity understanding from still pictures. Specifically, a Deep Belief Net is prepared to intertwine data from various boisterous sources, for example, body part recognition and item identification. To connect the semantic hole, we utilized physically marked information to significantly improve the strength of the pre-preparing and adjusting phases of the DBN preparing. The subsequent system is appeared to be vigorous to here and there inconsistent sources of info (e.g., loose location of human parts and questions), and beats the best in class approaches.
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