Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31693–31712 | Cite as

A computer assisted automatic grenade throw training system with simple digital cameras

  • Bin Liu
  • Yubo Ma
  • Yu Pei
  • Chao Wang
  • Chao WanEmail author


Grenade throwing is a routine military training and testing subject among many armies of the world. However, the conventional manual measurement mode has many defects: poor efficiency, large training field needing and important data losing. So how to utilize simple device and simple method framework to replace the actual test procedure becomes an interesting issue. In this paper, we present a real-time computer assisted grenade throwing training system by simple digital camera and low-cost computational methods. In this system, firstly, the marked grenade is extracted from the camera video frames; Secondly, a linked list is generated to store the grenade pixel coordinate; Thirdly, after a transformation from image space to real space, the instantaneous velocity of throwing (initial speed) can be computed; Lastly, a virtual 3D scene is established to demonstrate the training activity. By using this system, an overall throwing result data (distance, height, throwing angle, throwing speed and ballistic trajectory) can be obtained. The most significant novelty of our application is achieving a real-time computer assisted grenade throwing training system by simple and low-cost computational methods. In addition, we proposed a specialized self-adapted color enhancement method in the system. This computation strategy may provide some enlightenments for other research work. From the test data of several hurlers, it can be seen that the effectiveness and the accuracy of this training system are favorable. This system may provide technical support for the modern military throwing training and may change the traditional manual measuring mode for grenade throwing.


Grenade throwing Military training Camera video frame Virtual 3D scene Real-time system 



This work is supported by the National Natural Science Foundation of China (Nos. 61572101, 61300085), the Scientific Research Fund of Liaoning Provincial Education Department of China (No. L2013012) and the Fundamental Research Funds for the Central Universities of China (No. DUT14QY18). Thanks to Prof. Bingbing Zhang, Ms. Xiuyan Peng and Mr. Yuxiang Liu for providing training ground and training device and for help with camera calibration.

Supplementary material

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ESM 1 (MP4 13813 kb)


  1. 1.
    Abulrub AHG, Budabuss K, Mayer P, Williams MA (2013) The 3D immersive virtual reality technology use for spatial planning and public acceptance. Procedia Soc Behav Sci 75:328–337CrossRefGoogle Scholar
  2. 2.
    Chen C, Liu K, Kehtarnavaz N (2013) Real-time human action recognition based on depth motion maps. J Real-Time Image Proc:1–9Google Scholar
  3. 3.
    Cho K, Jung J, Lee SW, Lim SO, Yang HS (2011) Real-time recognition and tracking for augmented reality books. Comput Animat Virt W 22(6):529–541CrossRefGoogle Scholar
  4. 4.
    Cui J, Liu Y, Xu Y et al (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002CrossRefGoogle Scholar
  5. 5.
    Dardas NH, Silva JM, El Saddik A (2012) Target-shooting exergame with a hand gesture control. Multimed Tools Appl 1–23Google Scholar
  6. 6.
    Foley JD, Van Dam A (1982) Fundamentals of interactive computer graphics, vol 2. Addison-Wesley, ReadingGoogle Scholar
  7. 7.
    Jordt A, Koch R (2013) Direct model-based tracking of 3d object deformations in depth and color video. Int J Comput Vis 102(1–3):239–255MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kim M, Kim Y (2014) Development of an augmented reality puzzle game detecting hand posture using HSV color space in real time. J Korea Game Soc 14(5):79–86CrossRefGoogle Scholar
  9. 9.
    Koročsec D, Holobar A, Divjak M, Zazula D (2005) Building interactive virtual environments for simulated training in medicine using VRML and java/JavaScript. Comput Methods Prog Biomed 80:S61–S70CrossRefGoogle Scholar
  10. 10.
    Lampert CH, Peters J (2012) Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. J Real-Time Image Proc 7(1):31–41CrossRefGoogle Scholar
  11. 11.
    Liang JS (2012) Modeling an immersive VR driving learning platform in a web-based collaborative design environment. Comput Appl Eng Educ 20(3):553–567CrossRefGoogle Scholar
  12. 12.
    Liu Y, Cui J, Zhao H, et al (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, pp 898–901Google Scholar
  13. 13.
    Liu Y, Nie L, Han L et al (2015) Action2Activity: recognizing complex activities from sensor data. IJCAI 2015:1617–1623Google Scholar
  14. 14.
    Liu Y, Nie L, Liu L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  15. 15.
    Liu L, Cheng L, Liu Y et al (2016) Recognizing complex activities by a probabilistic interval-based model. AAAI 30:1266–1272Google Scholar
  16. 16.
    Liu Y, Zhang L, Nie L et al (2016) Fortune teller: predicting your career path. AAAI 2016:201–207Google Scholar
  17. 17.
    Liu Y, Zheng Y, Liang Y, et al (2016) Urban water quality prediction based on multi-task multi-view learningGoogle Scholar
  18. 18.
    Löcken A, Hesselmann T, Pielot M, Henze N, Boll S (2012) User-centred process for the definition of free-hand gestures applied to controlling music playback. Multimedia Systems 18(1):15–31CrossRefGoogle Scholar
  19. 19.
    Lok B, Ferdig RE, Raij A, Johnsen K, Dickerson R, Coutts J, ... Lind DS (2006) Applying virtual reality in medical communication education: current findings and potential teaching and learning benefits of immersive virtual patients. Virtual Reality 10(3–4):185–195CrossRefGoogle Scholar
  20. 20.
    Ma Z, Yang Y, Nie F, Sebe N, Yan S, Hauptmann AG (2014) Harnessing lab knowledge for real-world action recognition. Int J Comput Vis 109(1–2):60–73CrossRefGoogle Scholar
  21. 21.
    Mettin U, La Hera PX, Freidovich LB, Shiriaev AS, Helbo J (2008) Motion planning for humanoid robots based on virtual constraints extracted from recorded human movements. Intell Serv Robot 1(4):289–301CrossRefGoogle Scholar
  22. 22.
    Nielsen M, Störring M, Moeslund TB, Granum E (2004) A procedure for developing intuitive and ergonomic gesture interfaces for HCI. In: Gesture-based communication in human-computer interaction. Springer, Berlin Heidelberg, p 409–420CrossRefGoogle Scholar
  23. 23.
    Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artif Intell Rev 43(1):1–54CrossRefGoogle Scholar
  24. 24.
    Rudoy D, Zelnik-Manor L (2012) Viewpoint selection for human actions. Int J Comput Vis 97(3):243–254CrossRefGoogle Scholar
  25. 25.
    Saba T, Altameem A (2013) Analysis of vision based systems to detect real time goal events in soccer videos. Appl Artif Intell 27(7):656–667CrossRefGoogle Scholar
  26. 26.
    Shen Y, Gu PW, Ong SK, Nee AY (2012) A novel approach in rehabilitation of hand-eye coordination and finger dexterity. Virtual Reality 16(2):161–171CrossRefGoogle Scholar
  27. 27.
    Sun HM, Cheng WL (2009) The input-interface of webcam applied in 3D virtual reality systems. Comput Educ 53(4):1231–1240CrossRefGoogle Scholar
  28. 28.
    Trigueiros P, Ribeiro F, Reis LP (2014) Generic system for human-computer gesture interaction. In Autonomous Robot Systems and Competitions (ICARSC), 2014 IEEE International Conference on (p 175–180). IEEEGoogle Scholar
  29. 29.
    Yeo HS, Lee BG, Lim H (2015) Hand tracking and gesture recognition system for human-computer interaction using low-cost hardware. Multimed Tools Appl 74(8):2687–2715CrossRefGoogle Scholar
  30. 30.
    Zhang G, Qin X, An X, Chen W, Bao H (2006) As-consistent-as-possible compositing of virtual objects and video sequences. Comput Animat Virt W 17(3–4):305–314CrossRefGoogle Scholar
  31. 31.
    Zhang Y, Pettré J, Ondřej J, Qin X, Peng Q, Donikian S (2011) Online inserting virtual characters into dynamic video scenes. Comput Animat Virt W 22(6):499–510CrossRefGoogle Scholar
  32. 32.
    Zhu Y, Yuan B (2014) Real-time hand gesture recognition with Kinect for playing racing video games. In: Neural Networks (IJCNN), 2014 International Joint Conference on (p 3240–3246). IEEEGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bin Liu
    • 1
    • 2
  • Yubo Ma
    • 1
  • Yu Pei
    • 1
  • Chao Wang
    • 1
  • Chao Wan
    • 3
    Email author
  1. 1.International School of Information Science & Engineering (DUT-RUISE)Dalian University of TechnologyDalianChina
  2. 2.Key Lab of Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina
  3. 3.No.403 Clinical Department of PLA (People’s Liberation Army)DalianChina

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