Gimbal Tracking Control with Delayed Feedback of Target Information

  • Eunjin Koh
  • Jaekyu Lee
  • Junghyun Park
  • Jaewan Lim
  • Daeyeon KimEmail author
Original Article


In this paper, a power limited platform equipped with a gimbaled camera communicating with a remote station is considered. Sequence of the acquired images is downloaded to the station, and it specifies target of interest in the images. Then, target information, i.e., pixel coordinate of target in image, is sent back to the camera so image processor built in it can start target tracking and gimbal control. In this way, the burdensome task of specifying target is offloaded to the station, where task of tracking the specified target is operated by the camera. The target information is sent only once per target when the station assigns or reassigns the target to monitor. However, delay of the target information, invoked by the bandwidth limitation of the channel to the station, can disturb the image processor to start tracking. Therefore, we propose a method to remove the delay, using two image tracking tasks and an image buffer built in the camera. In addition, using the method, speed of the gimbal can be adjusted for reducing the risk of missing the target due to the camera motion.


Visual servoing Surveillance system Remote control 



  1. 1.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  2. 2.
    Cao X, Chen J, Xiao Y, Sun Y (2010) Building environment control with wireless sensor and actuator networks: centralized versus distributed. IEEE Trans Ind Electron 57(11):3596–3605CrossRefGoogle Scholar
  3. 3.
    Liu CH, Jayawardena S, Chen M, Perera C (2014) A survey on internet of things from industrial market perspective. IEEE Access 2:1660–1679CrossRefGoogle Scholar
  4. 4.
    Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W (2017) A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J 4(5):1125–1142CrossRefGoogle Scholar
  5. 5.
    Mozaffari M, Saad W, Bennis M, Debbah M (2017) Mobile unmanned aerial vehicles (UAVs) for energy-efficient internet of things communications. IEEE Trans Wirel Commun 16(11):7574–7589CrossRefGoogle Scholar
  6. 6.
    Motlagh NH, Bagaa M, Taleb T (2017) UAV-based IoT platform: a crowd surveillance use case. IEEE Commun Mag 55(2):128–134CrossRefGoogle Scholar
  7. 7.
    Lin CE, Yang S (2014) Camera gimbal tracking from UAV flight control. In: International automatic control conference, pp 319–322Google Scholar
  8. 8.
    Rajesh RJ, Ananda CM (2015) PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal. In: International conference on power and advanced control engineeringGoogle Scholar
  9. 9.
    Muhammad N, Bibi N, Wahabch A, Mahmood Z, Akrame T, Naqvie SR, Oh H, Kim D (2018) Image de-noising with subband replacement and fusion process using bayes estimators. Comput Electr Eng 70:413–427CrossRefGoogle Scholar
  10. 10.
    Muhammad N, Bibi N, Jahangir A, Mahmood Z (2018) Image denoising with norm weighted fusion estimators. Pattern Anal Appl 21(4):1013–1022MathSciNetCrossRefGoogle Scholar
  11. 11.
    Mughal B, Muhammad N, Sharif M, Rehman A, Saba T (2018) Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer 18:778CrossRefGoogle Scholar
  12. 12.
    Irshad M, Muhammad N, Sharif M, Yasmeen M (2018) Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation. Eur Phys J Plus 133(4):134–148CrossRefGoogle Scholar
  13. 13.
    Khan MA, Akram T, Sharif M, Javed MY, Muhammad N, Yasmin M (2018) An implementation of optimized framework for action classification using multilayers neural network on selected fused features. Pattern Anal Appl.
  14. 14.
    Naqvi SR, Akram T, Iqbal S, Haider SA, Kamran M, Muhammad N (2018) A dynamically reconfigurable logic cell: from artificial neural networks to quantum-dot cellular automata. Appl Nanosci 8(1–2):89–103CrossRefGoogle Scholar
  15. 15.
    Wu D, Ci S, Luo H, Ye Y, Wang H (2011) Video surveillance over wireless sensor and actuator networks using active cameras. IEEE Trans Autom Control 56(10):2467–2472MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Ma T, Hempel M, Peng D, Sharif H (2012) A survey of energy-efficient compression and communication techniques for multimedia in resource constrained systems. IEEE Commun Surv Tutor 15(3):963–972CrossRefGoogle Scholar
  17. 17.
    Hilkert JM (2008) Inertially stabilized platform technology: concepts and principles. IEEE Control Syst Mag 28(2):26–46MathSciNetzbMATHGoogle Scholar
  18. 18.
    Masten M (2008) Inertially stabilized platforms for optical imaging systems: tracking dynamic dynamic targets with mobile sensors. IEEE Control Syst Mag 28(2):47–64MathSciNetzbMATHGoogle Scholar
  19. 19.
    Hurak Z, Rezac M (2011) Image-based pointing and tracking for inertially stabilized airborne camera platform. IEEE Trans Control Syst Technol 20(5):1146–1159CrossRefGoogle Scholar
  20. 20.
    Micheloni C, Rinner B, Foresti GL (2010) Video analysis in pan-tilt-zoom camera networks. IEEE Signal Process Mag 27(5):78–90CrossRefGoogle Scholar
  21. 21.
    Villamizar M, Andrade-Cetto J, Sanfeliu A, Moreno-Noguer F (2017) Boosted random ferns for object detection. IEEE Trans Pattern Anal Mach Intell 40(2):272–288Google Scholar
  22. 22.
    Zhang T, Xu C, Yang M (2019) Robust structural sparse tracking. IEEE Trans Pattern Anal Mach Intell 41(2):473–486CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Eunjin Koh
    • 1
  • Jaekyu Lee
    • 1
  • Junghyun Park
    • 1
  • Jaewan Lim
    • 1
  • Daeyeon Kim
    • 1
    Email author
  1. 1.Agency for Defense DevelopmentDaejeonSouth Korea

Personalised recommendations