An Autonomous Intelligent Ornithopter

  • Sunita SuralkarEmail author
  • Smit Gangurde
  • Sanjeevkumar Chintakindi
  • Haresh Chawla
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)


The purpose of the system is to provide a powerful and intelligent surveillance tool to the police force so as to reduce crime. The law enforcement agencies have been motivated to use video surveillance systems to monitor and curb these threats. But this becomes a tedious task, prone to human errors. The core module of this system estimates the pose in humans present in the video and a backend capable of understanding the context as a whole. Many AI-powered surveillance systems are good at recognizing violent or malicious activity but fail to understand the context as a whole. We aim to understand the gradual change in human behavior in the given scenario, understand the confidence level of each expression and derive if the given scenario is truly violent or malicious. The Ornithopter is allowed to follow the suspect wherein the direction offsets are given by the server. The system differs from any state-of-the-art surveillance system as it provides aerial surveillance covering larger areas, and since the drone is bird-shaped, it can easily navigate the area without being easily detected. And as mentioned, the recognition of the true violent or malicious activity is context-based.


Ornithopter Deep learning Artificial intelligence Video analytics Human activity prediction 



This work was supported by Mumbai University’s Minor Research Grant.


  1. 1.
    Singh, A., Patil, D., Omkar, S.N.: Eye in the sky: real-time drone surveillance system (DSS) for violent individuals identification using ScatterNet hybrid deep learning network. To Appear in the IEEE Computer Vision and Pattern Recognition (CVPR) Workshops (2018)Google Scholar
  2. 2.
    Park, J.H., Yoon, K.-J.: Designing a biomimetic ornithopter capable of sustained and controlled flight. J. Bionic Eng. 5, 39–47 (2008)CrossRefGoogle Scholar
  3. 3.
    Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., Holzinger, A.: Human activity recognition using recurrent neural networks. In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE (2017)Google Scholar
  4. 4.
    Li, X., Chuah, M.C.: ReHAR: robust and efficient human activity recognition. arXiv:1802.09745
  5. 5.
    Hossain, M.S., Muhammad, G., Abdul, W., Song, B., Gupta, B.B.: Cloud-assisted secure video transmission and sharing framework for smart cities. Future Gener. Comput. Syst. 83(C), 596–606 (2018)CrossRefGoogle Scholar
  6. 6.
    Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: IEEE International Conference on Computer Vision (2017)Google Scholar
  7. 7.
    Chen, Y., Wang, Z., Peng, Y., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. arXiv:1711.07319v1, 20 November 2017
  8. 8.
    Jackowski, Z.J.: Design and Construction of Autonomous Ornithopter. Massachusetts Institute of Technology, Cambridge (2009)Google Scholar
  9. 9.
    Sifre, L., Mallat, S.: Rotation, scaling and deformation invariant scattering for texture discrimination. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2003)Google Scholar
  10. 10.
    Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., Baik, S.W.: Action recognition in video sequences using deep bi-directional LSTM with CNN features. Intelligent Media Laboratory, College of Software and Convergence Technology, Sejong University, Seoul, Republic of Korea (2018)Google Scholar
  11. 11.
    Borges, P.V.K., Conci, N., Cavallaro, A.: Video-based human behavior understanding: a survey. In: 2013 IEEE Conference Circuits and Systems for Video Technology (2003)Google Scholar
  12. 12.
    Robertson, N., Reid, I.: Behaviour understanding in video: a combined method. In: 2010 IEEE International Conference on Computer Vision (ICCV 2005) (2005)Google Scholar
  13. 13.
    Fan, Y., et al.: Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction. ACM (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sunita Suralkar
    • 1
    Email author
  • Smit Gangurde
    • 1
  • Sanjeevkumar Chintakindi
    • 1
  • Haresh Chawla
    • 1
  1. 1.VES Institute of TechnologyMumbaiIndia

Personalised recommendations