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Identifying Objects in Real-Time at the Lowest Framerate

Author

Listed:
  • Md. Mamun Hossain

    (Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh)

  • Md. Ashiqur Rahman

    (Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh)

  • Hitendra Dhargave

    (Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh)

Abstract

The practice of finding instances of semantic objects of a certain class, including people, cars, and traffic signs, in digital photos and videos is known as object identification or detection. Due to the development of high-resolution cameras and their widespread usage in everyday life, the detection is one of the most difficult and rapidly expanding study fields in computer science, particularly in computer vision. For automatic object recognition, several researchers have experimented with a variety of techniques, including image processing and computer vision. In this research, we employed a deep learning based framework YOLOv3 using Python, Tensorflow, and OpenCV to identify objects in real time. We do a number of tests using the COCO dataset to verify the effectiveness of the suggested strategy. The results of the experiments show that our suggested solution is resource and cost effective since it uses the fewest frames per second.

Suggested Citation

  • Md. Mamun Hossain & Md. Ashiqur Rahman & Hitendra Dhargave, 2022. "Identifying Objects in Real-Time at the Lowest Framerate," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 7(8), pages 60-63, August.
    • Handle: RePEc:bjf:journl:v:7:y:2022:i:8:p:60-63
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