Trajectory image based dynamic gesture recognition with convolutional neural networks
Ji-Ting Hu, Chunxiao Fan, Yue Ming · 2015
Robust dynamic gesture recognition algorithm is of great value for kinds of intelligent interactive systems. Most current researches on this field are based on trajectory time-series, which is unstable and limited. In this paper, we proposed a novel method to realize dynamic gesture recognition by analyzing the static trajectory images with Convolutional Neural Networks (CNN). First of all, a new motion-capture device named Leap Motion is used to track fingertip positions. An effective gesture spotting algorithm is applied to identify the start/end points of dynamic gestures. Then, we map the 3D fingertip coordinates to an image acquisition window frame by frame to get the corresponding trajectory images. After a series of preprocessing steps, the normalized trajectory images are fed to a CNN model. We test the performance of the proposed method on a self-built database, and experimental results show the effectiveness for dynamic gestures recognition of numbers 0-9, with the average recognition rate up to 98.8%.