Skip to main content
SpringerLink
Log in

Artificial Intelligence Techniques in Smart Cities Surveillance Using UAVs: A Survey

  • Chapter
  • First Online:
Machine Intelligence and Data Analytics for Sustainable Future Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 971))

Abstract

The security and urbanization challenge is expected to rise to 90% by 2050, and to leverage existing resources, technology is the solitary means to cope with this anticipated raise in entail. The Smart City is focused on the smooth convergence of Information and Communication Technology with the most technological innovations like well-connected home and equipment. Smart city augments the lifestyle of its residents by providing efficacious infrastructure and enhanced security. Surveillance is a recurring and monotonous assignment that descends the performance of human guards when continued for a longer period of time. Unmanned Aerial Vehicles (UAVs) or Drones can be deployed as security cameras to augment human guards. It can be deployed to track intruders, monitor unusual activities such as theft, violence and unprecedented corona-virus pandemic scenarios. UAV based visual surveillance in Smart cities, produces a huge amount of multimedia data. The need to process and analyze the data automatically in real-time is critical. Artificial Intelligence and Deep learning imitates human intelligence and provides excellent analytical capabilities to learn about complex data obtained in real environments. The integrated solution of Deep learning technology with the UAVs an electronic eye-in-the-sky has leveraged the capability of detection, recognition and deterrence in a scalable surveillance system. A comprehensive review on the potential benefits of UAVs and its applications for surveillance in smart cities has been presented. This chapter elaborates seamless integration of UAVs and Deep Learning technologies solutions for smart city surveillance. The paper concludes with a description of main challenges for the application of UAVs in deep learning solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697–713.

    Article  Google Scholar 

  2. Shahidehpour, M., Li, Z., & Ganji, M. (2018). Smart cities for a sustainable urbanization: Illuminating the need for establishing smart urban infrastructures. IEEE Electrification Magazine, 6(2), 16–33.

    Article  Google Scholar 

  3. Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China Information Sciences, 58(10), 1–18.

    Article  Google Scholar 

  4. Srinivasan, S., Latchman, H., Shea, J., Wong, T., & McNair, J. (2004). Airborne traffic surveillance systems: Video surveillance of highway traffic. In Proceedings of the ACM 2nd International Workshop on Video Surveillance & Sensor Networks (pp. 131–135).

    Google Scholar 

  5. Gurwicz, Y., Yehezkel, R., & Lachover, B. (2011). Multiclass object classification for real-time video surveillance systems. Pattern Recognition Letters, 32(6), 805–815.

    Article  Google Scholar 

  6. Alam, M. F., Katsikas, S., Beltramello, O., & Hadjiefthymiades, S. (2017). Augmented and virtual reality based monitoring and safety system: A prototype IoT platform. Journal of Network and Computer Applications, 89, 109–119.

    Google Scholar 

  7. Malik, P. K., Sharma, R., Singh, R., Gehlot, A., Satapathy, S. C., Alnumay, W.S., Pelusi, D., Ghosh, U., & Nayak, J. (2020). Industrial Internet of Things and its applications in industry 4.0: State of the art. Computer Communications.

    Google Scholar 

  8. Chen, J., Li, K., Deng, Q., Li, K., & Yu Philip, S. (2019). Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Transactions on Industrial Informatics.

    Google Scholar 

  9. Nawaratne, R., Alahakoon, D., De Silva, D., & Yu, X. (2019). Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Transactions on Industrial Informatics, 16(1), 393–402.

    Google Scholar 

  10. Motlagh, N. H., Bagaa, M., & Taleb, T. (2017). UAV-based IoT platform: A crowd surveillance use case. IEEE Communications Magazine, 55(2), 128–134.

    Google Scholar 

  11. Semsch, E., Jakob, M., Pavlicek, D., & Pechoucek, M. (2009). Autonomous UAV surveillance in complex urban environments. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (Vol. 2, pp. 82–85). IEEE.

    Google Scholar 

  12. Kopfstedt, T., Mukai, M., Fujita, M., & Ament, C. (2008). Control of formations of UAVs for surveillance and reconnaissance missions. IFAC Proceedings Volumes, 41(2), 5161–5166.

    Article  Google Scholar 

  13. Vattapparamban, E., Güvenç, İ., Yurekli, A. İ., Akkaya, K., & Uluağaç, S. (2016). Drones for smart cities: Issues in cybersecurity, privacy, and public safety. In 2016 International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 216–221). IEEE.

    Google Scholar 

  14. Li, J., Huang, W., Shao, L., & Allinson, N. (2014). Building recognition in urban environments: A survey of state-of-the-art and future challenges. Information Sciences, 277, 406–420.

    Article  Google Scholar 

  15. Richman, L. (2005). Human guard enhancing multiple site integrated security system. U.S. Patent 6,894,617, issued May 17, 2005.

    Google Scholar 

  16. Cuthbert, A. R. (1995). The right to the city: Surveillance, private interest and the public domain in Hong Kong. Cities, 12(5), 293–310.

    Article  Google Scholar 

  17. Richman, L., Vacaru, A., Zatusevschi, O., Belyshev, A., Oostendorp, M., Denisov, D., & Alexeev, K. (2003). Method and protocol for real time security system. U.S. Patent Application 10/176,565, filed November 6, 2003.

    Google Scholar 

  18. Peek-Asa, C., Schaffer, K. B., Kraus, J. F. & Howard, J. (1998). Surveillance of non-fatal workplace assault injuries, using police and employers’ reports. Journal of Occupational and Environmental Medicine, 40(8), 707–713.

    Google Scholar 

  19. Guo, S., He, T., Mokbel, M. F., Stankovic, J. A., & Abdelzaher, T. F. (2010). On accurate and efficient statistical counting in sensor-based surveillance systems. Pervasive and Mobile Computing, 6(1), 74–92.

    Article  Google Scholar 

  20. Aguilar-Ponce, R., Kumar, A., Luis Tecpanecatl-Xihuitl, J., & Bayoumi, M. (2007). A network of sensor-based framework for automated visual surveillance. Journal of Network and Computer Applications, 30(3), 1244–1271.

    Google Scholar 

  21. Bellazreg, R., Boudriga, N., & An, S. (2013). Border surveillance using sensor based thick-lines. In The International Conference on Information Networking 2013 (ICOIN) (pp. 221–226). IEEE.

    Google Scholar 

  22. Hu, T., Zhang, H., Zhu, X., Clunis, J., & Yang, G. (2018). Depth sensor based human detection for indoor surveillance. Future Generation Computer Systems, 88, 540–551.

    Article  Google Scholar 

  23. Shao, X., Zhao, H., Shibasaki, R., Shi, Y., & Sakamoto, K. (2011). 3D crowd surveillance and analysis using laser range scanners. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2036–2043). IEEE.

    Google Scholar 

  24. Aliakbarpour, H., Nunez, P., Prado, J., Khoshhal, K., & Dias, J. (2009). An efficient algorithm for extrinsic calibration between a 3d laser range finder and a stereo camera for surveillance. In 2009 International Conference on Advanced Robotics (pp. 1–6). IEEE.

    Google Scholar 

  25. Norris, C., & Armstrong, G. (1999). The maximum surveillance society: The rise of CCTV (Vol. 2). Oxford: Berg.

    Google Scholar 

  26. Levine, R. M. (2000). SIDE and closed circuit television (CCTV): Exploring surveillance in public space.

    Google Scholar 

  27. Nieto, M., Johnston-Dodds, K., & Simmons, C. W. (2002) Public and private applications of video surveillance and biometric technologies (Vol. 2, No. 6). Sacramento, CA: California State Library, California Research Bureau.

    Google Scholar 

  28. Kruegle, H. (2011). CCTV surveillance: Video practices and technology. Elsevier.

    Google Scholar 

  29. Norris, C., & Moran, J. (2016). Surveillance, closed circuit television and social control. Routledge.

    Google Scholar 

  30. McCahill, M. (2013). The surveillance web. Routledge.

    Google Scholar 

  31. Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure surveillance framework for IoT systems using probabilistic image encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679–3689.

    Google Scholar 

  32. Memos, V. A., Psannis, K. E., Ishibashi, Y., Kim, B.-G., & Gupta, B. B. (2018). An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework. Future Generation Computer Systems, 83, 619–628.

    Article  Google Scholar 

  33. Hu, L., & Ni, Q. (2017). IoT-driven automated object detection algorithm for urban surveillance systems in smart cities. IEEE Internet of Things Journal, 5(2), 747–754.

    Article  Google Scholar 

  34. Batra, I., Verma, S., Malik, A., Ghosh, U., Rodrigues, J. J. P. C., Nguyen, G. N., Hosen, A. S. M., & Mariappan, V. (2020). Hybrid logical security framework for privacy preservation in the green Internet of Things. Sustainability, 12(14), 5542.

    Google Scholar 

  35. Quadri, S. A. I., & Sathish, P. (2017). IoT based home automation and surveillance system. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 861–866). IEEE.

    Google Scholar 

  36. Sarkar, S., Totaro, M. W., Elgazzar. K. (2019). Intelligent drone-based surveillance: Application to parking lot monitoring and detection. In Unmanned Systems Technology XXI (Vol. 11021, p. 1102104). International Society for Optics and Photonics.

    Google Scholar 

  37. Sharma, V., You, I., Pau, G., Collotta, M., Lim, J. D., & Kim, J. N. (2018). LoRaWAN-based energy-efficient surveillance by drones for intelligent transportation systems. Energies, 11(3), 573.

    Google Scholar 

  38. Vannicola, V. C., & Mineo, J. A. (1988). Applications of knowledge based systems to surveillance. In Proceedings of the 1988 IEEE National Radar Conference (pp. 157–164). IEEE.

    Google Scholar 

  39. Rego, A., Canovas, A., Jiménez, J. M., & Lloret, J. (2018). An intelligent system for video surveillance in IoT environments. IEEE Access, 6, 31580–31598.

    Article  Google Scholar 

  40. Feldstein, S. (2019). The global expansion of AI surveillance (Vol. 17). Carnegie Endowment for International Peace.

    Google Scholar 

  41. Bilal, M., Khan, A., Khan, M. U. K., & Kyung, C.-M. (2016). A low-complexity pedestrian detection framework for smart video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology, 27(10), 2260–2273.

    Article  Google Scholar 

  42. Wei, H., & Kehtarnavaz, N. (2019). Semi-supervised faster RCNN-based person detection and load classification for far field video surveillance. Machine Learning and Knowledge Extraction, 1(3), 756–767.

    Article  Google Scholar 

  43. Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005). Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation science, 39(1), 1–24.

    Article  Google Scholar 

  44. Ghosh, U., Chatterjee, P., Shetty, S., & Datta, R. (2020). An SDN-IoT-based framework for future smart cities: Addressing perspective. In Internet of Things and secure smart environments: successes and pitfalls (p. 441). CRC Press.

    Google Scholar 

  45. Malik, A. A., Tosh, D. K., & Ghosh, U. (2019). Non-intrusive deployment of blockchain in establishing cyber-infrastructure for smart city. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 1–6). IEEE.

    Google Scholar 

  46. Laouira, M. L., Abdelli, A., Othman, J. B., & Kim, H. (2019). An efficient WSN based solution for border surveillance. IEEE Transactions on Sustainable Computing.

    Google Scholar 

  47. Shen, C., Xie, R., & Zhang, L. (2015). Temporal recursive pedestrian trajectory identification for multi-camera surveillance system. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (pp. 1158–1163). IEEE.

    Google Scholar 

  48. Lovas, T., & Barsi, Á. (2015). Pedestrian detection by profile laser scanning. In 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (pp. 408–412). IEEE.

    Google Scholar 

  49. Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal.

    Google Scholar 

  50. Garg, S., Singh, A., Batra, S., Kumar, N., & Yang, L. T. (2018). UAV-empowered edge computing environment for cyber-threat detection in smart vehicles. IEEE Network, 32(3), 42–51.

    Article  Google Scholar 

  51. De Paz, J. F., Bajo, J., Rodríguez, S., Villarrubia, G., & Corchado, J. M. (2016). Intelligent system for lighting control in smart cities. Information Sciences, 372, 241–255.

    Google Scholar 

  52. Mathur, S., & Modani, U. S. (2016). Smart city-a gateway for artificial intelligence in India. In 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1–3). IEEE.

    Google Scholar 

  53. Srivastava, S., Bisht, A., & Narayan, N. (2017). Safety and security in smart cities using artificial intelligence—A review. In 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence (pp. 130–133). IEEE.

    Google Scholar 

  54. Nikolopoulos, B., Dimitrakopoulos, G., Bravos, G., Dimopoulos, A., Nikolaidou, M., & Anagnostopoulos, D. (2016). Embedded intelligence in smart cities through multi-core smart building architectures: Research achievements and challenges. In 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS) (pp. 1–2). IEEE.

    Google Scholar 

  55. Toh, C. K. (2020). Security for smart cities. IET Smart Cities, 2(2), 95–104.

    Article  Google Scholar 

  56. Mojib, E. B. S., Bahalul Haque, A. K. M., Nafis Raihan, M., Rahman, M., & Alam, F. B. (2019). A novel approach for border security; Surveillance drone with live intrusion monitoring. In 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) (pp. 65–68). IEEE.

    Google Scholar 

  57. Makantasis, K., Karantzalos, K., Doulamis, A., & Doulamis, N. (2015). Deep supervised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4959–4962). IEEE (2015).

    Google Scholar 

  58. De-la-Torre, M., Granger, E., Radtke,P. V. W., Sabourin, R., & Gorodnichy, D. O. (2015). Partially-supervised learning from facial trajectories for face recognition in video surveillance. Information Fusion, 24, 31–53.

    Google Scholar 

  59. Sillito, R. R., & Fisher, R. B. (2008). Semi-supervised learning for anomalous trajectory detection. In BMVC (vol. 1, pp. 035–1).

    Google Scholar 

  60. Fenil, E., Manogaran, G., Vivekananda, G. N., Thanjaivadivel, T., Jeeva, S., & Ahilan, A. (2019). Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Computer Networks, 151, 191–200.

    Google Scholar 

  61. Ullah, A., Muhammad, K., Ser, J. D., Baik, S. W., & de Albuquerque, V. H. C. (2018). Activity recognition using temporal optical flow convolutional features and multilayer LSTM. IEEE Transactions on Industrial Electronics, 66(12), 9692–9702.

    Google Scholar 

  62. Ko, K.-E., & Sim, K.-B. (2018). Deep convolutional framework for abnormal behavior detection in a smart surveillance system. Engineering Applications of Artificial Intelligence, 67, 226–234.

    Article  Google Scholar 

  63. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 961–971).

    Google Scholar 

  64. Lu, N., Yidan, W., Feng, L., & Song, J. (2018). Deep learning for fall detection: Three-dimensional CNN combined with LSTM on video kinematic data. IEEE Journal of Biomedical and Health Informatics, 23(1), 314–323.

    Article  Google Scholar 

  65. Makris, D., & Ellis, T. (2005). Learning semantic scene models from observing activity in visual surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(3), 397–408.

    Google Scholar 

  66. Cobb, W. K., & Seow, M.-J. (2012). Unsupervised learning of temporal anomalies for a video surveillance system. U.S. Patent 8,167,430, issued May 1, 2012.

    Google Scholar 

  67. Venkatesan, R., Dinesh Anton Raja, P., & Balaji Ganesh, A. (2016). Unsupervised learning based video surveillance system established with networked cameras. In Advances in signal processing and intelligent recognition systems (pp. 603–614). Cham: Springer.

    Google Scholar 

  68. Seow, M.-J., & Cobb, W. K. (2015). Unsupervised learning of feature anomalies for a video surveillance system. U.S. Patent 9,111,148, issued August 18, 2015.

    Google Scholar 

  69. Munawar, A., Vinayavekhin, P., & De Magistris, G. (2017). Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1017–1025). IEEE.

    Google Scholar 

  70. Faigl, J., Váňa, P., Pěnička, R., & Saska, M. (2019). Unsupervised learning-based flexible framework for surveillance planning with aerial vehicles. Journal of Field Robotics, 36(1), 270–301.

    Article  Google Scholar 

  71. Julian, K. D., & Kochenderfer, M. J. (2019). Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning. Journal of Guidance, Control and Dynamics, 42(8), 1768–1778.

    Article  Google Scholar 

  72. Julian, K. D., & Kochenderfer, M. J. (2018). Autonomous distributed wildfire surveillance using deep reinforcement learning. In 2018 AIAA Guidance, Navigation, and Control Conference (p. 1589).

    Google Scholar 

  73. Zhao, D., Chen, Y., & Lv, L. (2016). Deep reinforcement learning with visual attention for vehicle classification. IEEE Transactions on Cognitive and Developmental Systems, 9(4), 356–367.

    Article  Google Scholar 

  74. Wei, H., Zheng, G., Yao, H., & Li, Z. (2018). Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2496–2505).

    Google Scholar 

  75. Aberkane, S., & Elarbi, M. (2019). Deep reinforcement learning for real-world anomaly detection in surveillance videos. In 2019 6th International Conference on Image and Signal Processing and Their Applications (ISPA) (pp. 1–5). IEEE.

    Google Scholar 

  76. Yan, C., Xiang, X., & Wang, C. (2019). Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments. Journal of Intelligent & Robotic Systems, 1–13.

    Google Scholar 

  77. Rao, Y., Lu, J., & Zhou, J. (2017). Attention-aware deep reinforcement learning for video face recognition. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3931–3940).

    Google Scholar 

  78. Ciresan, D., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2012). Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in neural information processing systems (pp. 2843–2851).

    Google Scholar 

  79. Liu, X., He, P., Chen, W., & Gao, J. (2019). Multi-task deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504.

  80. Deng, H. (2013). Guided random forest in the RRF package. arXiv preprint arXiv:1306.0237.

  81. Fukushima, K., & Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets (pp. 267–285). Berlin, Heidelberg: Springer.

    Google Scholar 

  82. Linnainmaa, S. (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master’s Thesis (in Finnish), University of Helsinki (pp. 6–7).

    Google Scholar 

  83. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.

    Article  Google Scholar 

  84. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

    Article  Google Scholar 

  85. Jackel, L. D., Lecun, Y., Stenard, C., Strom, B., Sharman, D., & Zuckert, D.(1998). Optical character recogntion for automatic teller machines. In Industrial applications of neural networks (pp. 375–378).

    Google Scholar 

  86. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).

    Google Scholar 

  87. Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818–833). Cham: Springer.

    Google Scholar 

  88. Bochkovskiy, A., Wang, C.-Y., Mark Liao, H.-Y. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

  89. Sreenu, G., & Saleem Durai, M. A. (2019). Intelligent video surveillance: a review through deep learning techniques for crowd analysis. Journal of Big Data, 6(1), 48.

    Google Scholar 

  90. Yazdi, M., & Bouwmans, T. (2018). New trends on moving object detection in video images captured by a moving camera: A survey. Computer Science Review, 28, 157–177.

    Article  MathSciNet  Google Scholar 

  91. Bah, M. D., Hafiane, A., & Canals, R. (2018). Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sensing, 10(11), 1690.

    Google Scholar 

  92. Akhloufi, M. A., Castro, N. A., & Couturier, A. (2018). UAVs for wildland fires. In Autonomous systems: Sensors, vehicles, security, and the Internet of Everything (Vol. 10643, p. 106430M). International Society for Optics and Photonics.

    Google Scholar 

  93. Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018). Occlusion-aware R-CNN: Detecting pedestrians in a crowd. In Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics) (pp. 657–674).

    Google Scholar 

  94. Lee, D., Kim, G., Kim, D., Myung, H., & Choi, H.-T. (2012). Vision-based object detection and tracking for autonomous navigation of underwater robots. Ocean Engineering, 48, 59–68.

    Article  Google Scholar 

  95. Yang, S., Luo, P., Loy, C. C., & Tang, X. (2015). From facial parts responses to face detection: A deep learning approach. Proceedings of the IEEE International Conference on Computer Vision, 2015(3), 3676–3684.

    Google Scholar 

  96. Yao, L., & Wang, B. (2019). Pedestrian detection framework based on magnetic regional regression. IET Image Process.

    Google Scholar 

  97. Tian, B., Li, Y., Li, B., & Wen, D. (2014). Rear-view vehicle detection and tracking by combining multiple parts for complex Urban surveillance. IEEE Transactions on Intelligent Transportation Systems, 15(2), 597–606.

    Article  Google Scholar 

  98. Hayat, S., Yanmaz, E., & Muzaffar, R. (2016). Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint. IEEE Communications Surveys Tutorials, 18(4), 2624–2661.

    Article  Google Scholar 

  99. Sanchez-Lopez, J. L., Molina, M., Bavle, H., Sampedro, C., Suárez Fernández, R. A., & Campoy, P. (2017). A multi-layered component-based approach for the development of aerial robotic systems: the aerostack framework. Journal of Intelligent & Robotic Systems, 88(2–4), 683–709.

    Google Scholar 

  100. Carrio, A., Sampedro, C., Rodriguez-Ramos, A., & Campoy, P. (2017). A review of deep learning methods and applications for unmanned aerial vehicles. Journal of Sensors.

    Google Scholar 

  101. Lin, C., He, D., Kumar, N., Raymond Choo, K.-K., Vinel, A., & Huang, X. (2018). Security and privacy for the internet of drones: Challenges and solutions. IEEE Communications Magazine, 56(1), 64–69.

    Google Scholar 

  102. Wazid, M., Das, A. K., & Lee, J.-H. (2018). Authentication protocols for the internet of drones: Taxonomy, analysis and future directions. Journal of Ambient Intelligence and Humanized Computing, 1–10.

    Google Scholar 

Download references

Acknowledgments

This work titled “Artificial Intelligence and Deep Learning Technologies in Smart Cities Surveillance using UAVs” is supported by the grant from Department of Science and Technology, Government of India, against CFP launched under Interdisciplinary Cyber Physical Systems (ICPS) Programme, DST/ICPS/CPS-Individual/2018/181(G).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Narina Thakur, Preeti Nagrath, Rachna Jain, Dharmender Saini & Nitika Sharma

  2. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

Authors
  1. Narina Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Preeti Nagrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachna Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dharmender Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nitika Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. D. Jude Hemanth
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of EECS, Vanderbilt University, Nashville, TN, USA

    Uttam Ghosh

  2. ENSAK, Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Yassine Maleh

  3. Charles Darwin University, Darwin, NT, Australia

    Mamoun Alazab

  4. Department of Computer Science and Engineering, Independent University, Dhaka, Bangladesh

    Al-Sakib Khan Pathan

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Thakur, N., Nagrath, P., Jain, R., Saini, D., Sharma, N., Hemanth, D.J. (2021). Artificial Intelligence Techniques in Smart Cities Surveillance Using UAVs: A Survey. In: Ghosh, U., Maleh, Y., Alazab, M., Pathan, AS.K. (eds) Machine Intelligence and Data Analytics for Sustainable Future Smart Cities. Studies in Computational Intelligence, vol 971. Springer, Cham. https://doi.org/10.1007/978-3-030-72065-0_18

Download citation

Publish with us

Policies and ethics

Search

Navigation