Paper Info
Title
Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier
Abstract
In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.
Year
DOI
Venue
2015
10.3390/s150612410
SENSORS
Keywords
Field
DocType
human pose estimation,gesture recognition,depth information,low-cost platform
Human-body model,Computer vision,Central processing unit,Gesture,Computer science,Support vector machine,Gesture recognition,Pose,Artificial intelligence,Svm classifier
Journal
Volume
Issue
ISSN
15
6.0
1424-8220
Citations 
PageRank 
References 
11
0.66
15
Authors
6
Name
Order
Citations
PageRank
Hanguen Kim1304.87
sangwon lee2172.17
d lee3110.66
soonmin choi4110.66
jinsun ju5110.66
Hyun Myung629062.59