Journal
PROCEEDING OF THE SEVENTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION (IHCI 2015)
Volume 84, Issue -, Pages 6-13Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2016.04.059
Keywords
dynamic gestures; trajectory recognition; finger detection; reference vector
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This work demonstrates a real time framework to recognize trajectories articulated in the air using bare hand motion. A frontend is established to plot the trajectories as well as to spot the interleaved dynamic gestures. Finger detection based controls and trajectory plotting velocity help to spot the gesture boundaries. Trajectories are described through a unique Equi-Polar Signature (EPS) derived from circular grid normalization of trajectory points. EPS is invariant to translation, scale, rotation and stroke directions. k-Nearest Neighbor (KNN) classification strategy recognizes EPSs of digits 0-9 and operator symbols '+', '-', 'x', and '/'. Unlike previous path alignment algorithms, the proposed EPS scheme executes in linear time and fits to real-time constraints. On a customized depth video dataset of 2280 trajectories, 94.1% recognition accuracy is achieved. (C) 2016 The Authors. Published by Elsevier B.V.
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