Detection of Human Behaviour by Object Recognition using Deep Learning: A Review

Authors

  • Utsab Mukherjee Assistant Professor, Department of Computer Science, Bhawanipur Education Society College, West Bengal, India. Author
  • Samir Kumar Bandyopadhyay Academic Advisor, Bhawanipur Education Society College, West Bengal, India Author

Keywords:

Deep Learning, Object Detection, Neural Network, and Object Recognition

Abstract

The major drawback of the society is the  falsehood of human nature; so prediction of nature of the  individual by analysing the video or image of that person  is highly necessary. From the post World War II period  due to the advancement of technology since the past few  decades many countries have developed low cost cameras  with high resolution. They generally use the RGB and  depth features to enhance the image quality captured by  the camera. Hence object recognition is the new branch of  computer science which emerges. It has got a close  relationship with image understanding and analysis of  video; which encourages several researchers to work in  this domain since past few years. As the branch of deep  learning develops handful of efficient tools have been  develop which prove to be highly efficient to learn high  level deeper features of the image and semantics. In this  paper a review on object recognition using deep learning  has been discussed. This paper includes basics of deep  learning and object recognition. Then we have discussed  some tools required to perform object recognition as well  as moving object recognition and finally some future  goals of this subject have been discussed. 

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Published

2020-03-25

Issue

Vol. 8 No. 2 (2020): International Journal of Innovative Research in Computer Science and Technology (Mar) 2020

Section

Articles

How to Cite

Detection of Human Behaviour by Object Recognition using Deep Learning: A Review. (2020). International Journal of Innovative Research in Computer Science & Technology, 8(2), 16–19. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13333