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Sensors 2015, 15(6), 12410-12427; doi:10.3390/s150612410

Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

1
Urban Robotics Laboratory (URL), Dept. Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-338, Korea
2
Image & Video Research Group, Samsung S1 Cooperation, 168 S1 Building, Soonhwa-dong,Joong-gu, Seoul 100-773, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 23 March 2015 / Accepted: 20 May 2015 / Published: 26 May 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [3083 KB, uploaded 26 May 2015]   |  

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. View Full-Text
Keywords: human pose estimation; gesture recognition; depth information; low-cost platform human pose estimation; gesture recognition; depth information; low-cost platform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kim, H.; Lee, S.; Lee, D.; Choi, S.; Ju, J.; Myung, H. Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier. Sensors 2015, 15, 12410-12427.

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