Open Access
| Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
|---|---|---|
| Article Number | 14004 | |
| Number of page(s) | 7 | |
| Section | VLSI, Artificial Intelligence and Physics | |
| DOI | https://doi.org/10.1051/e3sconf/202454014004 | |
| Published online | 21 June 2024 | |
- S. Cherian, & C. Singh, “Real Time Implementation of Object Tracking Through webcam,” Internation Journal of Research in Engineering and Technology, 128–132, (2014). [Google Scholar]
- Z. Zhao, Q. Zheng, P. Xu, S. T, & X. Wu, “Object detection with deep learning: A review,” IEEE transactions on neural networks and learning systems, 30(11), 3212–3232, (2019). [CrossRef] [Google Scholar]
- N. Dalal, & B. Triggs, “Histograms of oriented gradients for human detection,” In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 1, pp. 886–893). IEEE, (2005, June). [Google Scholar]
- R. Girshick., J. Donahue, T. Darrell, & J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE transactions on pattern analysis and machine intelligence, 38(1), 142–158, (2015). [Google Scholar]
- X. Wang, A. Shrivastava, & A. Gupta, “A-fast-rcnn: Hard positive generation via adversary for object detection,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2606–2615), (2017). [Google Scholar]
- S. Ren, K. H, R. Girshick, & J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” In Advances in neural information processing systems (pp. 91–99), (2015). [Google Scholar]
- J. Redmon, S. Divvala, R. Girshick, & A. Farhadi, “You only look once: Unified, realtime object detection,” In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788), (2016). [Google Scholar]
- J. Redmon, & A. Farhadi, “YOLO9000: better, faster, stronger,” In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263–7271) (2017). [Google Scholar]
- J. Redmon & A. Farhadi, “Yolov3: An incremental improvement,” ArXiv preprint arXiv: 1804.02767, (2018). [Google Scholar]
- R. Bharti, K. Bhadane, P. Bhadane, & A. Gadhe, “Object Detection and Recognition for Blind Assistance,” International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395–0056 Volume: 06, (2019). [Google Scholar]
- T. Lin, Y. Maire, M. Belongie, S. Hays, J. Perona, P. Ramanan, D., & C.L. Zitnick, “Microsoft coco: Common objects in context,” In European conference on computer vision (pp. 740–755). Springer, Cham, (2014, September). [Google Scholar]
- Lowe D., “Distinctive image features from scale-invariant keypoints, “ Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004. [CrossRef] [Google Scholar]
- Dalal N. and Triggs B., “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2005, pp. 886–893. [Google Scholar]
- Everingham M., van Gool L., Williams C. K. I., Winn J., and Zisserman A., “The PASCAL visual object classes (VOC) challenge,” Int. J. Comput. Vis., vol. 80, no. 2, pp. 303–338, 2010. [CrossRef] [Google Scholar]
- Fukushima K., “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biol. Cybern., vol. 36, no. 4, pp. 193–202, 1980. [CrossRef] [PubMed] [Google Scholar]
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