An AI Powered Computer Vision Application for Airport CCTV Users

Mehmet Cemal Atlıoğlu; Gökhan Koç

An AI Powered Computer Vision Application for Airport CCTV Users

Abstract

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.

Keywords

artificial intelligence, machine learning, deep learning, human detection, airport, security, CNN, CCTV

References

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

Details

Primary Language

English

Subjects

Machine Learning Algorithms

Journal Section

Research Article

Authors

Mehmet Cemal Atlıoğlu ^* This is me
Türkiye

Gökhan Koç This is me
Türkiye

Publication Date

July 1, 2021

Submission Date

April 8, 2021

Acceptance Date

June 25, 2021

Published in Issue

Year 2021 Volume: 4 Number: 1

IZ

https://izlik.org/JA84WF87NN

APA

Atlıoğlu, M. C., & Koç, G. (2021). An AI Powered Computer Vision Application for Airport CCTV Users. Data Science and Applications, 4(1), 21-26. https://izlik.org/JA84WF87NN

AMA

1.Atlıoğlu MC, Koç G. An AI Powered Computer Vision Application for Airport CCTV Users. DataSCI. 2021;4(1):21-26. https://izlik.org/JA84WF87NN

Chicago

Atlıoğlu, Mehmet Cemal, and Gökhan Koç. 2021. “An AI Powered Computer Vision Application for Airport CCTV Users”. Data Science and Applications 4 (1): 21-26. https://izlik.org/JA84WF87NN.

EndNote

Atlıoğlu MC, Koç G (July 1, 2021) An AI Powered Computer Vision Application for Airport CCTV Users. Data Science and Applications 4 1 21–26.

IEEE

[1]M. C. Atlıoğlu and G. Koç, “An AI Powered Computer Vision Application for Airport CCTV Users”, DataSCI, vol. 4, no. 1, pp. 21–26, July 2021, [Online]. Available: https://izlik.org/JA84WF87NN

ISNAD

Atlıoğlu, Mehmet Cemal - Koç, Gökhan. “An AI Powered Computer Vision Application for Airport CCTV Users”. Data Science and Applications 4/1 (July 1, 2021): 21-26. https://izlik.org/JA84WF87NN.

JAMA

1.Atlıoğlu MC, Koç G. An AI Powered Computer Vision Application for Airport CCTV Users. DataSCI. 2021;4:21–26.

MLA

Atlıoğlu, Mehmet Cemal, and Gökhan Koç. “An AI Powered Computer Vision Application for Airport CCTV Users”. Data Science and Applications, vol. 4, no. 1, July 2021, pp. 21-26, https://izlik.org/JA84WF87NN.

Vancouver

1.Mehmet Cemal Atlıoğlu, Gökhan Koç. An AI Powered Computer Vision Application for Airport CCTV Users. DataSCI [Internet]. 2021 Jul. 1;4(1):21-6. Available from: https://izlik.org/JA84WF87NN