Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 4 , 30 May 2023


Open Access | Article

Computer vision model’s application in the current system on object detection tasks

Feilian Huang * 1
1 Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 4, 1-6
Published 30 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Feilian Huang. Computer vision model’s application in the current system on object detection tasks. ACE (2023) Vol. 4: 1-6. DOI: 10.54254/2755-2721/4/20230335.

Abstract

The implementation of object detection algorithms would be helpful to the various fields of the current time. When object detection is applied to the surveillance camera system, it will be more efficient to locate crimes or find lost kids. This paper will investigate the performance of different object detection algorithms in a real-world scenario. With experimentation, CenterNet++ outperforms YOLO and MaskRCNN, two traditional and classic object detection algorithms, on the MS COCO dataset, which concludes that CenterNet++ can ensure both accuracy and speed.

Keywords

Video Processing Systems, Computer Vision, Artificial Intelligence, Object Detection

References

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2. Whittaker, Danielle.2021 “Why AI CCTV Is the Future of Security and Surveillance in Public Spaces.” Security, 14 Dec. 2021.

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4. Arunnehru, J., et al. “Human Action Recognition Using 3D Convolutional Neural Networks with 3D Motion Cuboids in Surveillance Videos.” Procedia Computer Science, vol. 133, 2018, pp. 471–477.

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10. Wei, Chen & Fan, Haoqi & Xie, Saining & Wu, Chao-Yuan & Yuille, Alan & Feichtenhofer, Christoph. 2021. Masked Feature Prediction for Self-Supervised Visual Pre-Training.

11. Lin, Tsung-Yi & Maire, Michael & Belongie, Serge & Hays, James & Perona, Pietro & Ramanan, Deva & Dollár, Piotr & Zitnick, C. 2014. Microsoft COCO: Common Objects in Context. 8693.

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13. Duan, Kaiwen & Bai, Song & Xie, Lingxi & Qi, Honggang & Tian, Qi. 2022 CenterNet++ for Object Detection.

14. Xingyi Zhou, Dequan Wang, Philipp Krähenbühl. Objects as Points arXiv preprint arXiv:1904.07850

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16. He, Kaiming & Gkioxari, Georgia & Dollar, Piotr & Girshick, Ross. 2017 Mask R-CNN. 2980-2988.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-55-3
ISBN (Online)
978-1-915371-56-0
Published Date
30 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/4/20230335
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated