Skip to main content
SpringerLink
Log in

Single Horizontal Camera-Based Object Tracking Quadcopter Using StaGaus Algorithm

  • Conference paper
  • First Online:
Emerging Research in Computing, Information, Communication and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 882))

  • 802 Accesses

Abstract

This research is a solution to the problem of a hardware platform of object tracking drones. This platform can be used to build further advancements such as selfie drones, follow-me drones for adventure sports and robot pets. StaGaus algorithm has been derived using first principles and is compared against standard algorithms like SRDCF. The algorithm is tested on, an on-board two Android phones for real-time telemetry. This paper demonstrates that StaGaus works even on memory- and performance-constrained devices. In order to standardise the development of object tracking drones’ algorithms, we have built a customised ROS-based Gazebo simulator from scratch. This simulator is capable of simulating multiple robots of multiple types. This uses actual physics Open Dynamics Engine which has been compared against the existing simulators. Finally, as a by-product, this design proved that the cost of the proposed physical quadcopter is low.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: A survey. ACM Computing Surveys, 38(4), Article Number 13. https://doi.org/10.1145/1177352.1177355.

    Article  Google Scholar 

  2. Smeulders, A. W. M, Chu, D. M., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. (2014). Visual tracking: an experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7). https://doi.org/10.1109/tpami.2013.230.

  3. Yang, H., Shao, L., Zheng, F., Wang, L., & Song, Z. (2011). Recent advances and trends in visual tracking: A review. Neurocomputing, 74(18). https://doi.org/10.1016/j.neucom.2011.07.024.

    Article  Google Scholar 

  4. Li, R., Pang, M., Zhao, C., Zhou, G., & Fang, L. (2016). Monocular long-term target following on UAVs. CVPRW. https://doi.org/10.1109/cvprw.2016.11.

  5. Staranowicz, A., & Mariottini, G. L. (2011). A survey and comparison of commercial and open-source robotic simulator software. PETRA 11. In Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments, Article No. 56. https://doi.org/10.1145/2141622.2141689.

  6. Blasco, P. I., Diaz, F., del Rio, M., Ternero, C. R., Muniz, D. C., & Diaz, S. V. (2012). Robotics software frameworks for multi-agent robotic systems development. Robotics and Autonomous Systems, 60(6), 803–821. https://doi.org/10.1016/j.robot.2012.02.004.

    Article  Google Scholar 

  7. Mueller, M., Smith, N., & Ghanem, B. (2016). A benchmark and simulator for UAV tracking. In Computer vision, ECCV. https://doi.org/10.1007/978-3-319-46448-0_27.

    Chapter  Google Scholar 

  8. Ivaldi, S., Padois, V., & Nori, F. (2014). Tools for dynamics simulation of robots: a survey based on user feedback, Humanoid Robots (Humanoids). In 2014 14th IEEE-RAS International Conference. https://doi.org/10.1109/humanoids.2014.7041462.

  9. APM 2.6 Documentation. http://ardupilot.org/copter/docs/common-apm25-and-26-overview.html.

  10. Rother, C., Kolmogorov, V., & Blake, A. (2004). GrabCut interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, SIGGRAPH. https://doi.org/10.1145/1015706.1015720.

    Article  Google Scholar 

  11. Greig, D. M., Porteus, B. T., & Seheult, A. H. (1989). Exact maximum a posteriori estimation for binary images. Wiley for Royal Statistical Society, 51(2), 271–279.

    Google Scholar 

  12. Color Models, Image Color Conversion, Volume 2, Image processing, Intel Integrated Performance Primitives for Intel Architecture Developer Reference. https://software.intel.com/en-us/node/503873.

  13. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT and SURF, Willow Garage. In ICCV Proceedings of the 2011 International Conference on Computer Vision, pp. 2564–2571. https://doi.org/10.1109/iccv.2011.6126544.

  14. Configuring your Environment, Erle Robotics. http://docs.erlerobotics.com/simulation/configuring_your_environment.

  15. Seeed Studio’s Webcam Android Application. https://github.com/xiongyihui/Webcam.

  16. Our core softwares for physical quadrotor. https://github.com/traquad.

Download references

Acknowledgements

We thank Vladimir Ermakov, leading developer of MAVROS for assistance regarding MAVROS. We thank Venkat, Edall Systems, for assistance regarding PWM RC values and also Harsha H N and Amrinder S R for their support in programming.

Author information

Authors and Affiliations

  1. Department of ECE, Ramaiah Institute of Technology, Bengaluru, 560054, India

    Lakshmi Shrinivasan & N. R. Prasad

Authors
  1. Lakshmi Shrinivasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. R. Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lakshmi Shrinivasan .

Editor information

Editors and Affiliations

  1. Chancellor, Central University of Karnataka, Kalaburagi, Karnataka, India

    N. R. Shetty

  2. INSA Senior Scientist, National Institute of Advanced Studies,, Bangalore, Karnataka, India

    L. M. Patnaik

  3. Principal, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    H. C. Nagaraj

  4. Professor, Nitte Meenakshi Inst of Tech, Bangalore, Karnataka, India

    Prasad Naik Hamsavath

  5. Professor, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    N. Nalini

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shrinivasan, L., Prasad, N.R. (2019). Single Horizontal Camera-Based Object Tracking Quadcopter Using StaGaus Algorithm. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5953-8_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5952-1

  • Online ISBN: 978-981-13-5953-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation