Corrigendum Algorithm for Gesture Recognition Based on Multiple Information Fusion and Kinect

Abstract

Gesture recognition is an important and challenging task in the field of computer vision. Starting from the 3D shape of coding gestures, it puts forward a new kind of gesture recognition framework based on depth image. It extracts the space characteristics of a variety of 3D point cloud based on Kinect, including local principal components analysis on point cloud to get the histogram of main component, gradient direction histogram based on local depth difference and depth distribution histogram of local point cloud. Principal component histogram and gradient direction histogram effectively coding the local shape of gestures, depth distribution histogram compensates the loss of the shaping descriptor information. Through preliminary training of random forest classifier to filter the characteristics, and characteristics with less influence on classification results are removed, thus the computational costs are reduced. The filtered characteristics are used for training of random forest classifier again to classify gestures. Experiment is carried on two large-scale gesture data sets, for more difficult ASL dataset, the proposed method has improved the recognition rate of 3.6% then the best previous algorithm.