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1711294 Vol 6 · Issue 7 Download Paper

Development of an Object Detection in Live Video Using Tensorflow

Jimoh Babatunde Olawale

Subject area: Science,Engineering and Technology  ·  Area of research: Tensorflow

DOI: https://doi.org/10.64388/IREV6I7-1711294-6013

Abstract

This is an android-based system for object detection. It provides object detection in the near area for video challenged users. Object detection is the process of detecting and defining objects of a certain known class in an image. This system helps visually impaired people to identify any objects in his or her environment like a cars, house, cat, dog, chair, table, phone, laptop etc. Here, we have explored the possibility of implementing object detector on an ubiquities mobile devices powdered by Android capable of maintaining real time frame rate while keeping high precision using Tensorflow Object detection API and pretrained ssd mobilenet v2 model. This system proven to be more efficient for object detecting challenged people.

References

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[3] Baohua Qiang, Ruidong Chen, Mingliang Zhou, Yuanchao Pang, Yijie Zhai and Minghao Yang, “Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images” retrieved [Online] from www.mdpi.com/journal/sensors.

[4] Chandan G, Ayush Jain, Harsh Jain, Mohana, “Real Time Object Detection and Tracking Using Deep Learning and OpenCV”, International Conference on Inventive Research in Computing Applications (ICIRCA 2018) pp.1305-1308.

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[7] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy, “Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[8] S. Kamate and N. Yilmazer, “Application of Object Detection and Tracking Techniques for Unmanned Aerial Vehicles,” in Procedia Computer Science, 2015, vol. 61, pp. 436–441.

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[11] “TensorFlow Lite Object Detection” Tensorflow Lite” [Online] Available https://www.tensorflow.org/lite/models/object_detection/overview [Accessed: 13-Jan-2021]

[12] Zhong-Qiu Zhao, Shou-tao Xu, Xindong Wu, Object Detection with Deep Learning: A Review, College of Computer Science and Information Engineering, Hefei University of Technology, Apr - 2019, China.

How to cite this paper

Jimoh Babatunde Olawale "Development of an Object Detection in Live Video Using Tensorflow" Iconic Research And Engineering Journals Volume 6 Issue 7 2023 Page 594-600 https://doi.org/10.64388/IREV6I7-1711294-6013
Jimoh Babatunde Olawale "Development of an Object Detection in Live Video Using Tensorflow" Iconic Research And Engineering Journals, vol. 6, no. 7, Jan. 2023, doi: https://doi.org/10.64388/IREV6I7-1711294-6013
Jimoh Babatunde Olawale (2023). Development of an Object Detection in Live Video Using Tensorflow. Iconic Research And Engineering Journals, 6(7). doi: https://doi.org/10.64388/IREV6I7-1711294-6013
Jimoh Babatunde Olawale "Development of an Object Detection in Live Video Using Tensorflow" Iconic Research And Engineering Journals, vol. 6, no. 7, Jan. 2023. Crossref, https://doi.org/10.64388/IREV6I7-1711294-6013
@article{1711294,
      author = {Jimoh Babatunde Olawale},
      title = {Development of an Object Detection in Live Video Using Tensorflow},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {594-600},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711294.pdf},
      abstract = {This is an android-based system for object detection. It provides object detection in the near area for video challenged users. Object detection is the process of detecting and defining objects of a certain known class in an image. This system helps visually impaired people to identify any objects in his or her environment like a cars, house, cat, dog, chair, table, phone, laptop etc.  Here, we have explored the possibility of implementing object detector on an ubiquities mobile devices powdered by Android capable of maintaining real time frame rate while keeping high precision using Tensorflow Object detection API and pretrained ssd mobilenet v2 model. This system proven to be more efficient for object detecting challenged people.},
      month = {January},
      doi = {https://doi.org/10.64388/IREV6I7-1711294-6013}
  }