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Hand gesture tracking and recognition system using Lucas–Kanade algorithms for control of consumer electronics

Journal Article


Abstract


  • Dynamic hand gesture tracking and recognition system can simplify the way humans interact with computers and many other non-critical consumer electronic equipments. This system is based on the well-known “Wave Controller” technology developed at the University of Wollongong [1], [2] and [3] and certainly a step forward in video gaming and consumer electronics control interfaces. Currently, computer interfacing mainly involves keyboard, mouse, joystick or gaming wheels and occasionally voice recognition for user input. These modes of interaction have constrained the artistic ability of many users, as they are required to respond to the computer through pressing buttons or moving other apparatus. Voice recognition is seen as unreliable and impractical in areas where more than one user is present. All these drawbacks can be tackled by using a reliable hand gesture tracking and recognition system based on both Lucas–Kanade and Moment Invariants approaches. This will facilitate interaction between users and computers and other consumer electronic equipments in real time. This will further enhance the user experience as users are no longer have any physical connection to the equipment being controlled. In this research, we have compared our proposed moment invariant based algorithm with template based and Fourier descriptor based methods to highlight the advantages and limitations of the proposed system.

UOW Authors


  •   Premaratne, Prashan
  •   Ajaz, Sabooh (external author)
  •   Premaratne, Malin (external author)

Publication Date


  • 2013

Citation


  • P. Premaratne, S. Ajaz & M. Premaratne, "Hand gesture tracking and recognition system using Lucas–Kanade algorithms for control of consumer electronics," Neurocomputing, vol. 116, pp. 242-249, 2013.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2104

Number Of Pages


  • 7

Start Page


  • 242

End Page


  • 249

Volume


  • 116

Place Of Publication


  • http://www.sciencedirect.com/science/article/pii/S092523121200714X

Abstract


  • Dynamic hand gesture tracking and recognition system can simplify the way humans interact with computers and many other non-critical consumer electronic equipments. This system is based on the well-known “Wave Controller” technology developed at the University of Wollongong [1], [2] and [3] and certainly a step forward in video gaming and consumer electronics control interfaces. Currently, computer interfacing mainly involves keyboard, mouse, joystick or gaming wheels and occasionally voice recognition for user input. These modes of interaction have constrained the artistic ability of many users, as they are required to respond to the computer through pressing buttons or moving other apparatus. Voice recognition is seen as unreliable and impractical in areas where more than one user is present. All these drawbacks can be tackled by using a reliable hand gesture tracking and recognition system based on both Lucas–Kanade and Moment Invariants approaches. This will facilitate interaction between users and computers and other consumer electronic equipments in real time. This will further enhance the user experience as users are no longer have any physical connection to the equipment being controlled. In this research, we have compared our proposed moment invariant based algorithm with template based and Fourier descriptor based methods to highlight the advantages and limitations of the proposed system.

UOW Authors


  •   Premaratne, Prashan
  •   Ajaz, Sabooh (external author)
  •   Premaratne, Malin (external author)

Publication Date


  • 2013

Citation


  • P. Premaratne, S. Ajaz & M. Premaratne, "Hand gesture tracking and recognition system using Lucas–Kanade algorithms for control of consumer electronics," Neurocomputing, vol. 116, pp. 242-249, 2013.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2104

Number Of Pages


  • 7

Start Page


  • 242

End Page


  • 249

Volume


  • 116

Place Of Publication


  • http://www.sciencedirect.com/science/article/pii/S092523121200714X