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u00a0Akshit Rawat,
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nJanuary 9, 2023 at 5:31 am
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nAbstract
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The emergence and continued reliance on the Internet and related technologies has resulted in massive amounts of data that can be analysed. Humans, on the other hand, do not have the cognitive abilities to comprehend such vast amounts of data. Machine learning (ML) is a mechanism that enables humans to process large amounts of data, gain insights into the data’s behaviour, and make more informed decisions based on the analysis’s results. ML has a wide range of applications, such as efficient and accurate object detection, and has been a hot topic in the advancement of computer vision systems. Since the introduction of deep learning techniques, the accuracy of object detection has increased dramatically. The project intends to incorporate cutting-edge object detection techniques with the goal of achieving high accuracy with real-time performance. The reliance on other computer vision techniques to assist the deep learning-based approach is a major challenge in many object detection systems, resulting in slow and suboptimal performance. The resulting system is fast and accurate, making it useful for applications that require object detection.
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Keywords Recognition, Detection, OpenCV, E-R, DFD
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References
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1. Joshi P, Escrivá DM, Godoy V. OpenCV By Example. Packt Publishing Ltd; 2016.
2. Khan M, Chakraborty S, Astya R, Khepra S. Face Detection and Recognition Using OpenCV. In2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) 2019:116-119. IEEE.
3. Culjak I, Abram D, Pribanic T, Dzapo H, Cifrek M. A brief introduction to OpenCV. In2012 proceedings of the 35th international convention MIPRO 2012:1725-1730. IEEE.
4. Gornale SS, Babaleshwar AK, Yannawar PL. Analysis and detection of content based video retrieval. Int. J. Image, Graph. Signal Process. 2019;11(3):43.
5. Harini V, Prahelika V, Sneka I, Adlene Ebenezer P. Hand gesture recognition using OpenCv and Python. InInternational Conference On Computational Vision and Bio Inspired Computing 2018:1711-1719. Springer, Cham.
6. Howse J, Joshi P, Beyeler M. Opencv: computer vision projects with python. Packt Publishing Ltd; 2016 Oct 24.
7. Gollapudi S. Object Detection and Recognition. In Learn Computer Vision Using OpenCV 2019:97-117. Apress, Berkeley, CA.
8. Brostow GJ, Fauqueur J, Cipolla R. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters. 2009 Jan 15;30(2):88-97.
9. Brostow GJ, Fauqueur J, Cipolla R. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters. 2009;30(2):88-97.
10. Yu L, Sun W, Wang H, Wang Q, Liu C. The design of single moving object detection and recognition system based on OpenCV. In2018 IEEE International Conference on Mechatronics and Automation (ICMA) 2018:1163-1168. IEEE.”
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Journal Menu
Editors Overview
ijippr maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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- By [foreach 286]n
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Akshit Rawat
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- Student,Department of Computer Science & Engineering, Dr. M.C. Saxena College of Engineering & Technology,Uttar Pradesh,India
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Abstract
nThe emergence and continued reliance on the Internet and related technologies has resulted in massive amounts of data that can be analysed. Humans, on the other hand, do not have the cognitive abilities to comprehend such vast amounts of data. Machine learning (ML) is a mechanism that enables humans to process large amounts of data, gain insights into the data’s behaviour, and make more informed decisions based on the analysis’s results. ML has a wide range of applications, such as efficient and accurate object detection, and has been a hot topic in the advancement of computer vision systems. Since the introduction of deep learning techniques, the accuracy of object detection has increased dramatically. The project intends to incorporate cutting-edge object detection techniques with the goal of achieving high accuracy with real-time performance. The reliance on other computer vision techniques to assist the deep learning-based approach is a major challenge in many object detection systems, resulting in slow and suboptimal performance. The resulting system is fast and accurate, making it useful for applications that require object detection.n
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Keywords: Recognition, Detection, OpenCV, E-R, DFD
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Image Processing and Pattern Recognition(ijippr)]
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Full Text
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Browse Figures
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References
n[if 1104 equals=””]
1. Joshi P, Escrivá DM, Godoy V. OpenCV By Example. Packt Publishing Ltd; 2016.
2. Khan M, Chakraborty S, Astya R, Khepra S. Face Detection and Recognition Using OpenCV. In2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) 2019:116-119. IEEE.
3. Culjak I, Abram D, Pribanic T, Dzapo H, Cifrek M. A brief introduction to OpenCV. In2012 proceedings of the 35th international convention MIPRO 2012:1725-1730. IEEE.
4. Gornale SS, Babaleshwar AK, Yannawar PL. Analysis and detection of content based video retrieval. Int. J. Image, Graph. Signal Process. 2019;11(3):43.
5. Harini V, Prahelika V, Sneka I, Adlene Ebenezer P. Hand gesture recognition using OpenCv and Python. InInternational Conference On Computational Vision and Bio Inspired Computing 2018:1711-1719. Springer, Cham.
6. Howse J, Joshi P, Beyeler M. Opencv: computer vision projects with python. Packt Publishing Ltd; 2016 Oct 24.
7. Gollapudi S. Object Detection and Recognition. In Learn Computer Vision Using OpenCV 2019:97-117. Apress, Berkeley, CA.
8. Brostow GJ, Fauqueur J, Cipolla R. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters. 2009 Jan 15;30(2):88-97.
9. Brostow GJ, Fauqueur J, Cipolla R. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters. 2009;30(2):88-97.
10. Yu L, Sun W, Wang H, Wang Q, Liu C. The design of single moving object detection and recognition system based on OpenCV. In2018 IEEE International Conference on Mechatronics and Automation (ICMA) 2018:1163-1168. IEEE.”
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International Journal of Image Processing and Pattern Recognition
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| Volume | 7 |
| Issue | 2 |
| Received | December 22, 2021 |
| Accepted | December 28, 2021 |
| Published | December 31, 2021 |
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