Structured dynamic time warping for continuous hand trajectory gesture recognition

Continuous hand gesture recognition is an important area of HCI and challenged by various writing habits and unconstrained hand movement. In this paper, we propose a Structured Dynamic Time Warping (SDTW) approach for continuous hand trajectory recognition. We first propose an automatic continuous trajectory segmentation approach which combines templates and velocity information to spot the beginning and ending points in hand gesture trajectories. Then we assign different weights to feature sequences based on the structured information, from the positions of corner points in the arbitrary trajectories. Finally, we evaluate the SDTW on the Continuous Letter Trajectory (CLT) database. Experimental results show that the proposed approach is robust to the diversity of same handwritten letter, and significantly outperforms state-of-the-art approaches.