Detection of Real Time Objects Using TensorFlow and OpenCV

  • Ajay Talele
  • Aseem Patil
  • Bhushan Barse

Abstract

Detecting and recognizing objects in unstructured as
well as structured environments is one of the most challenging
tasks in computer vision and artificial intelligence research.
This paper introduces a new computer vision-based obstacle
detection method for mobile technology and its applications.
Each individual image pixel is classified as belonging either to
an obstacle based on its appearance. The method uses a single
lens webcam camera that performs in real-time, and also
provides a binary obstacle image at high resolution. In the
adaptive mode, the system keeps learning the appearance of
the obstacle during operation. The system has been tested
successfully in a variety of environments, indoors as well as
outdoors, making it suitable for all kinds of hurdles. It also
tells us the type of obstacle which has been detected by the
system.

Keywords: Object Detection, Image Edge Detection, Image Segmentation, object recognition, computer vision

References

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How to Cite
Talele, A., Patil, A., & Barse, B. (2019). Detection of Real Time Objects Using TensorFlow and OpenCV. Asian Journal For Convergence In Technology (AJCT). Retrieved from http://www.asianssr.org/index.php/ajct/article/view/783
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