Recognition of human gait in oblique and frontal views using Kinect

Abstract

This study describes the recognition of human gait in the oblique and frontal views using novel gait features derived from the skeleton joints provided by Kinect. In D skeleton joints were extracted directly from the Kinect, which generates the the other hand, H-joint distance is a feature of distance between the hip joint with other skeleton joints. Prior to the gait feature extraction, the skeleton joints provided by Kinect were pre-processed in order to standardize the size of gait feature within a full gait cycle. To classify gait patterns according to its own group, a multi-layer perceptron was employed in the pattern recognition stage. Results show that a perfect recognition of human gait (100%) was attained for the frontal view using the feature of H-joint distance at the optimal multi

Convolutional Neural Network (CNN) based Gait Recognition System using Microsoft Kinect Skeleton Features

“…The data were obtained from [7]. It contains three-dimensional skeletal joint points of 23 subjects recorded by a Microsoft Kinect depth camera.…”