Real-Time Robust and Cost-Efficient Hand Tracking in Colored Video Using Simple Camera

  • Richa GolashEmail author
  • Yogendra Kumar Jain
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 100)


Dynamic hand gesture recognition field has high potential to change the interaction mechanism between human and machine. But user interfaces (UIs) working on hand movements are still a challenge because of the lack of cost-effective and robust hand tracking techniques. To avoid the challenges encountered in tracking, non-rigid and subtle object-Hand, researchers use advance cameras which overall increases the cost and complexity of any technique. In this paper, we have focused on two important stages of dynamic hand tracking, first is hand modeling and second is robust hand tracking. We have developed a prototype of hand tracking using graphical user interface (GUI) of MATLAB software, working on live videos captured using a normal camera. The proposed system is tested on videos in intelligent biometric group hand tracking (IBGHT) database.


Human gesture recognition Computer vision Feature extraction SIFT Normal camera Depth images Hand tracking 


  1. 1.
    Wachs JP, Kölsch M, Stern H, Edan Y (2011) Vision-based hand-gesture applications. Commun ACM 54:60–71 CrossRefGoogle Scholar
  2. 2.
    Yang S, Premaratne P, Vial P (2013) Hand gesture recognition: an overview. In: Proceedings 5th IEEE, IC-BNMT, Guilin, China, pp 63–69Google Scholar
  3. 3.
    Pisharady PK, Saerbeck M (2015) Recent methods and databases in vision-based hand gesture recognition: a review. Comput Vis Image Und 141:152–165CrossRefGoogle Scholar
  4. 4.
    Stergiopoulou E, Sgouropoulos K, Nikolaou N, Papamarkos N, Mitianoudis N (2014) Real time hand detection in a complex background. Eng App Artif Intel 35:54–70CrossRefGoogle Scholar
  5. 5.
    De Smedt Q, Wannous H, Vandeborre JP (2016) Skeleton-based dynamic hand gesture recognition. In: Proceedings IEEE-CVPRW, Las Vegas, USA, pp 1–9Google Scholar
  6. 6.
    Chong Y, Huang J, Pan S (2016) Hand gesture recognition using appearance features based on 3D point cloud. J Softw Eng Appl 9:103–111CrossRefGoogle Scholar
  7. 7.
    Suarez J, Murphy RR (2012) Hand gesture recognition with depth images: a review. In: Proceedings 2012 IEEE RO-MAN, Paris, France, pp 411–417Google Scholar
  8. 8.
    Fu Q, Santello M (2010) Tracking whole hand kinematics using extended Kalman filter. In: Annual international conference of the IEEE engineering in medicine and biology, pp 4606–4609Google Scholar
  9. 9.
    Park S, Yu S, Kim J, Kim S, Lee S (2012) 3D hand tracking using Kalman filter in depth space. EURASIP J Adv Signal Process 2012(1):36CrossRefGoogle Scholar
  10. 10.
    Shan C, Wei Y, Tan T, Ojardias F (2004) Real time hand tracking by combining particle filtering and mean shift. In: IEEE international conference on automatic face and gesture recognition, pp 669–674Google Scholar
  11. 11.
    Asaari MS, Rosdi BA, Suandi SA (2015) Adaptive Kalman filter incorporated eigen hand (AKFIE) for real-time hand tracking system. Multimed Tools Appl 74:9231–9257CrossRefGoogle Scholar
  12. 12.
    Joo SI, Weon SH, Choi HI (2014) Real-time depth-based hand detection and tracking. Sci World, Article ID 284827,, vol 17
  13. 13.
    Kovalenko M, Antoshchuk S, Sieck J (2014) Real-time hand tracking and gesture recognition using semantic-probabilistic network. In: Computer modelling and simulation (UKSim), IEEE, pp 269–274Google Scholar
  14. 14.
    Bao J, Song A, Guo Y, Tang H (2011) Dynamic hand gesture recognition based on SURF tracking. In: Electric information and control engineering (ICEICE), pp 338–341Google Scholar
  15. 15.
    Yao Y, Li CT (2013) Real-time hand gesture recognition for uncontrolled environments using adaptive SURF tracking and hidden conditional random fields. In: Proceedings ISVC 2013, Crete, Greece, pp 29–31Google Scholar
  16. 16.
    Wang X, Hänsch R, Ma L, Hellwich O (2014) Comparison of different color spaces for image segmentation using graph-cut. In: Proceedings 2014 International Conference on VISAPP, Lisbon, Portugal, pp 301–308Google Scholar
  17. 17.
    Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vision 60:91–110CrossRefGoogle Scholar
  18. 18.
    Lin W, Wu Y, Hung W, Tang V (2013) A study of real-time hand gesture recognition using SIFT on binary images. Adv Intell Syst Appl, pp 235–246Google Scholar
  19. 19.
    Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends® Comput Graph Vis 3:177–280Google Scholar
  20. 20.
    Sykora P, Kamencay P, Hudec R (2014) Comparison of SIFT and SURF methods for use on hand gesture recognition based on depth map. AASRI Procedia 9:19–24CrossRefGoogle Scholar
  21. 21.
    Asaari MS, Rosdi BA, Suandi SA (2014) Intelligent biometric group hand tracking (IBGHT) database for visual hand tracking research and development. Multimed Tools Appl 70:1869–1898CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.E&I DepartmentSamrat Ashok Technological InstituteVidishaIndia

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