SMART HUMAN FOLLOWING BABY STROLLER USING COMPUTER VISION

Authors

  • Md. Johir Raihan Electronics and Communication Engineering Discipline, Khulna University, Khulna-9208, Bangladesh
  • Md. Tariq Hasan Electronics and Communication Engineering Discipline, Khulna University, Khulna-9208, Bangladesh
  • Abdullah-Al Nahid Electronics and Communication Engineering Discipline, Khulna University, Khulna-9208, Bangladesh

DOI:

https://doi.org/10.53808/KUS.2022.ICSTEM4IR.0154-se

Keywords:

Baby Stroller, Computer Vision, Machine Learning (ML).

Abstract

A baby stroller is a vehicle that is used by people to carry their toddlers making their life easier or carrying out other activities. However, such a vehicle still requires human labor to carry from one place to another place. Moreover, carrying such a vehicle can be troublesome for elderly people, disabled persons, or pregnant women. We live in an era where every aspect of human life is becoming simpler with the use of an autonomous machine. The recent development in the field of Artificial Intelligence (AI) and Machine Learning (ML) has allowed us to achieve autonomous surveillance robots to autonomous self-driving cars. This advancement of AI and ML can also be implemented in such a way that can help a person carry their babies for them which reduces their labor. This paper proposes a human following baby carrier car that can follow a human without any kind of help from the person. This reduces the labor work for a person and provides much freedom to carry out their activities. Such a carrier can be operated by elderly people, disabled people, pregnant women, or by any other without the need for much labor. The proposed system relies on the computer vision analysis that uses the camera feed to detect the person to be followed and sends a command to a microcontroller that operates the Carrier.

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References

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Published

18-10-2022

How to Cite

[1]
M. J. . Raihan, M. T. . Hasan, and A.-A. . Nahid, “SMART HUMAN FOLLOWING BABY STROLLER USING COMPUTER VISION”, Khulna Univ. Stud., pp. 8–18, Oct. 2022.

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