An AI Powered Computer Vision Application for Airport CCTV Users
Yıl 2021,
Cilt: 4 Sayı: 1, 21 - 26, 30.06.2021
Mehmet Cemal Atlıoğlu
Gökhan Koç
Öz
Investments in aviation were experiencing difficult times due to the Covid-19 pandemic, and poverty drives the industry to generate value with existing products. Therefore, technology providers modernize legacy systems with AI add-ons like the usage of old CCTV cameras for securities operations even if they are not designed for these purposes [1]. In this study, the detection of objects such as people, luggage, and vehicles are executed and tested for the aviation ecosystem with a real-time computer vision application built on existing CCTV cameras. Also, the detection performance measurements and achievements of the application are shared.
Kaynakça
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- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2015). Region-based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence, 38(1), 142-158.
- He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.
- Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497.
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
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- Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).
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- Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
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- Zhang, Y., & Funkhouser, T. (2018). Deep depth completion of a single rgb-d image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 175-185).
- LeCun, Y., Jackel, L. D., Bottou, L., Brunot, A., Cortes, C., Denker, J. S., ... & Vapnik, V. (1995, October). Comparison of learning algorithms for handwritten digit recognition. In International conference on artificial neural networks (Vol. 60, pp. 53-60).
- Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
- Darknet: Open source neural networks in c. Available at: https://github.com/AlexeyAB/darknet
- Rajasegarar, S., Leckie, C., Palaniswami, M., & Bezdek, J. C. (2007, June). Quarter sphere based distributed anomaly detection in wireless sensor networks. In 2007 IEEE International Conference on Communications (pp. 3864-3869). IEEE.
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Yıl 2021,
Cilt: 4 Sayı: 1, 21 - 26, 30.06.2021
Mehmet Cemal Atlıoğlu
Gökhan Koç
Kaynakça
- Milne, R. J., Delcea, C., Cotfas, L. A., & Ioanăş, C. (2020). Evaluation of boarding methods adapted for social distancing when using apron buses. IEEE Access, 8, 151650-151667.
- Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055.
- Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008, June). A discriminatively trained, multiscale, deformable part model. In 2008 IEEE conference on computer vision and pattern recognition (pp. 1-8). IEEE.
- Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.
- Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.
- Dalal, N., & Triggs, B. (2005, June). 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.
- Girshick, R. B. (2012). From rigid templates to grammars: Object detection with structured models. Chicago, IL, USA: University of Chicago, Division of the Physical Sciences, Department of Computer Science.
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2015). Region-based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence, 38(1), 142-158.
- He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.
- Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497.
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
- Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016, October). Ssd: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Springer, Cham.
- Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).
- Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018). Single-shot refinement neural network for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4203-4212).
- Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
- Zhang, T., Gao, C., Ma, L., Lyu, M., & Kim, M. (2019, October). An empirical study of common challenges in developing deep learning applications. In 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) (pp. 104-115). IEEE.
- Junior, J. C. S. J., Musse, S. R., & Jung, C. R. (2010). Crowd analysis using computer vision techniques. IEEE Signal Processing Magazine, 27(5), 66-77.
- Mousavi, H., Mohammadi, S., Perina, A., Chellali, R., & Murino, V. (2015, January). Analyzing tracklets for the detection of abnormal crowd behavior. In 2015 IEEE Winter Conference on Applications of Computer Vision (pp. 148-155). IEEE.
- Smeureanu, S., & Ionescu, R. T. (2018, September). Real-time deep learning method for abandoned luggage detection in video. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 1775-1779). IEEE.
- Liu, C., Gu, J., Kim, K., Narasimhan, S. G., & Kautz, J. (2019). Neural rgb (r) d sensing: Depth and uncertainty from a video camera. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10986-10995).
- Zhang, Y., & Funkhouser, T. (2018). Deep depth completion of a single rgb-d image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 175-185).
- LeCun, Y., Jackel, L. D., Bottou, L., Brunot, A., Cortes, C., Denker, J. S., ... & Vapnik, V. (1995, October). Comparison of learning algorithms for handwritten digit recognition. In International conference on artificial neural networks (Vol. 60, pp. 53-60).
- Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
- Darknet: Open source neural networks in c. Available at: https://github.com/AlexeyAB/darknet
- Rajasegarar, S., Leckie, C., Palaniswami, M., & Bezdek, J. C. (2007, June). Quarter sphere based distributed anomaly detection in wireless sensor networks. In 2007 IEEE International Conference on Communications (pp. 3864-3869). IEEE.
- Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934