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Research Article
Object Detection Using Mobile Camera
Yuvaraj J1
Gokulnithi R2
Varshan V S3
Buvana M4
123 B. Tech IT, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India. 4Assistant Professor, Dept of IT, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
Published Online: March-April 2023
Pages: 388-392
Cite this article
↗ 10.59256/ijire2023040206
References
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Sons, 2018.
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Catalunya, 2017.
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CNN." arXiv preprint arXiv:1805.00331 (2018).
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International Journal for Light and Electron Optics 130 (2017): 935-944.
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arXiv:1704.04861 (2017).
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Computer Vision and Pattern Recognition (CVPR).IEEE, 2017.
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10. Veiga, Francisco José Lopes. "Image Processing for Detection of Vehicles In Motion." (2018). Huazhong Zhang, Han Hu, Guangyao,
Yongkang Wen, Kyle Guan, "Deepqoe: A Unified Framework for Learning to Predict Video QoE", Multimedia and Expo (ICME) 2018
IEEE International Conference on, pp. 1- 6, 2018.
11. Shijian Tang and Ye Yuan, “Object Detection based on Conventional Neural Network”. R.P.S.Manikandan, A. M. Kalpana, "A study
on feature selection in big data", Computer Communication and Informatics (ICCCI) 2017 International Conference on, pp. 1-5, 2017
Warde Farley, David. "Feedforward deep architectures for classification and synthesis." (2018).
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Computer Science & Engineering (IJEECSE), Special Issue - ICSCAAIT-2018|E-ISSN:2348-2273|P-ISSN:2454-1222,pp-138
140.American Diabetes Association.Standards of medical care in diabetes. Diabetes Care. 2009;32(supplement 1):S13–S61.utility
99.CALIFORNIA UNIVBERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2014.
2. K Saleh, Imad, Mehdi Ammi, and Samuel Szoniecky, eds. Challenges of the Internet of Things: Technique, Use, Ethics. John Wiley &
Sons, 2018.
3. Petrov, Yordan. Improving object detection by exploiting semantic relations between objects.MS thesis.UniversitatPolitècnica de
Catalunya, 2017.
4. Nikouei, SeyedYahya, et al. "Intelligent Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight
CNN." arXiv preprint arXiv:1805.00331 (2018).
5. Thakar, Kartikey, et al. "Implementation and analysis of template matching for image registration on DevKit- 8500D." Optik
International Journal for Light and Electron Optics 130 (2017): 935-944.
6. Bradski, Gary, and Adrian Kaehler.Learning OpenCV: Computer vision with the OpenCV library." O'Reilly Media, Inc.", 2008.
7. Howard, Andrew G., et al. "Mobile nets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint
arXiv:1704.04861 (2017).
8. Kong, Tao, et al. "Ron: Reverse connection with abjectness prior networks for object detection." 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR).IEEE, 2017.
9. Liu, Wei, et al. "Ssd: Single shot multi box detector." European conference on computer vision. Springer, Cham, 2016.
10. Veiga, Francisco José Lopes. "Image Processing for Detection of Vehicles In Motion." (2018). Huazhong Zhang, Han Hu, Guangyao,
Yongkang Wen, Kyle Guan, "Deepqoe: A Unified Framework for Learning to Predict Video QoE", Multimedia and Expo (ICME) 2018
IEEE International Conference on, pp. 1- 6, 2018.
11. Shijian Tang and Ye Yuan, “Object Detection based on Conventional Neural Network”. R.P.S.Manikandan, A. M. Kalpana, "A study
on feature selection in big data", Computer Communication and Informatics (ICCCI) 2017 International Conference on, pp. 1-5, 2017
Warde Farley, David. "Feedforward deep architectures for classification and synthesis." (2018).
12. Shilpisingh et al” An Analytic approach for 3D Shape descriptor for face recognition”, International Journal of Electrical, Electronics
Computer Science & Engineering (IJEECSE), Special Issue - ICSCAAIT-2018|E-ISSN:2348-2273|P-ISSN:2454-1222,pp-138
140.American Diabetes Association.Standards of medical care in diabetes. Diabetes Care. 2009;32(supplement 1):S13–S61.utility
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