A Study of Hand Motion/Posture Recognition in Two-Camera Views

  • Jingya Wang
  • Shahram PayandehEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


This paper presents a vision-based approach for hand gesture recognition which combines both trajectory recognition and hand posture recognition. With two calibrated cameras, the 3D hand motion trajectory can be reconstructed. The reconstructed trajectory is then modeled by dynamic movement primitives (DMP) and a support vector machine (SVM) is trained to recognize five classes of gestures trajectories. Scale-invariant feature transform (SIFT) is used to extract features on segmented hand postures taken from both camera views. Based on various hand appearances captured by the two cameras, the proposed hand posture recognition method has shown a very good success rate. A gesture vector is proposed to combine the recognition result from both trajectory and hand postures. For our experimental set-up, it was shown that it is possible to accomplish a good overall accuracy for gesture recognition.


Support Vector Machine Gesture Recognition Hand Gesture Camera View Hand Gesture Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Experimental Robotics and Imaging Laboratory, School of Engineering ScienceSimon Fraser UniversityBurnabyCanada

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