Dynamic Sign Language Recognition basedon Depth Motion Volumesand Key Frames

Abstract

This paper proposes a dynamic sign language recognition approach through the use of Depth Motion Volumes (DMV). Different from the previous research by stacking the difference of the motion energy of depth images, a novel strategy is applied for forming DMV. The selected key frames of the entire video sequences are stacked in chronological order and then projected into three orthogonal planes. Distinctive features are calculated by Histograms of Oriented Gradients (HOG) and then input to LIBSVM for recognition. The experiments are executed on Microsoft Research Gesture3D dataset and the results indicate that the proposed approach is efficient and performs better than the compared approaches