Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information
Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color informa...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2018-01-01
|
Series: | Journal of Sensors |
Online Access: | http://dx.doi.org/10.1155/2018/5809769 |
id |
doaj-72aab8f2d4c5408ea844c5f6fedd3fed |
---|---|
record_format |
Article |
spelling |
doaj-72aab8f2d4c5408ea844c5f6fedd3fed2020-11-24T22:24:29ZengHindawi LimitedJournal of Sensors1687-725X1687-72682018-01-01201810.1155/2018/58097695809769Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth InformationXuhong Ma0Jinzhu Peng1School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001, ChinaSchool of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001, ChinaGesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.http://dx.doi.org/10.1155/2018/5809769 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xuhong Ma Jinzhu Peng |
spellingShingle |
Xuhong Ma Jinzhu Peng Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information Journal of Sensors |
author_facet |
Xuhong Ma Jinzhu Peng |
author_sort |
Xuhong Ma |
title |
Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information |
title_short |
Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information |
title_full |
Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information |
title_fullStr |
Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information |
title_full_unstemmed |
Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information |
title_sort |
kinect sensor-based long-distance hand gesture recognition and fingertip detection with depth information |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-725X 1687-7268 |
publishDate |
2018-01-01 |
description |
Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances. |
url |
http://dx.doi.org/10.1155/2018/5809769 |
work_keys_str_mv |
AT xuhongma kinectsensorbasedlongdistancehandgesturerecognitionandfingertipdetectionwithdepthinformation AT jinzhupeng kinectsensorbasedlongdistancehandgesturerecognitionandfingertipdetectionwithdepthinformation |
_version_ |
1725760995855433728 |