The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-5/W6
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W6, 1–8, 2015
https://doi.org/10.5194/isprsarchives-XL-5-W6-1-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W6, 1–8, 2015
https://doi.org/10.5194/isprsarchives-XL-5-W6-1-2015

  18 May 2015

18 May 2015

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

M. Favorskaya, A. Nosov, and A. Popov M. Favorskaya et al.
  • Institute of Informatics and Telecommunications, Siberian State Aerospace University, 31 Krasnoyarsky Rabochy av., Krasnoyarsk, 660014 Russian Federation

Keywords: Dynamic Gesture, Skeleton Representation, Motion History, Gesture Recognition

Abstract. Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset “Multi-modal Gesture Recognition Challenge 2013: Dataset and Results” including 393 dynamic hand-gestures was chosen. The proposed method yielded 84–91% recognition accuracy, in average, for restricted set of dynamic gestures.