Real-time Gesture Recognition Method Research Articles

Spatial and temporal pyramid-based real-time gesture recognition

This paper proposes a novel method for real-time gesture recognition. Aiming at improving the effectiveness and accuracy of HGR, spatial pyramid is applied to linguistically segment gesture sequence into linguistic units and a temporal pyramid is proposed to get a time-related histogram for each single gesture. Those two pyramids can help to extract more comprehensive information of human gestures from RGB and depth video. A two-layered HGR is further exploited to further reduce the computation complexity. The proposed method obtains high accuracy and low computation complexity performance on the ChaLearn Gesture Dataset, comprising more than 50, 000 gesture sequences recorded.

A novel finger and hand pose estimation technique for real-time hand gesture recognition

This paper presents a high-level hand feature extraction method for real-time gesture recognition. Firstly, the fingers are modelled as cylindrical objects due to their parallel edge feature. Then a novel algorithm is proposed to directly extract fingers from salient hand edges. Considering the hand geometrical characteristics, the hand posture is segmented and described based on the finger positions, palm center location and wrist position. A weighted radial projection algorithm with the origin at the wrist position is applied to localize each finger. The developed system can not only extract extensional fingers but also flexional fingers with high accuracy. Furthermore, hand rotation and finger angle variation have no effect on the algorithm performance. The orientation of the gesture can be calculated without the aid of arm direction and it would not be disturbed by the bare arm area. Experiments have been performed to demonstrate that the proposed method can directly extract high-level hand feature and estimate hand poses in real-time.

Online gesture recognition from pose kernel learning and decision forests

The recent popularization of real time depth sensors has diversified the potential applications of online gesture recognition to end-user natural user interface (NUI). This requires significant robustness of the gesture recognition to cope with the noisy data from the popular depth sensor, while the quality of the final NUI heavily depends on the recognition execution speed. This work introduces a method for real-time gesture recognition from a noisy skeleton stream, such as those extracted from Kinect depth sensors. Each pose is described using an angular representation of the skeleton joints. Those descriptors serve to identify key poses through a Support Vector Machine multi-class classifier, with a tailored pose kernel. The gesture is labeled on-the-fly from the key pose sequence with a decision forest, which naturally performs the gesture time control/warping and avoids the requirement for an initial or neutral pose. The proposed method runs in real time and its robustness is evaluated in several experiments.

Real-time gesture recognition using eigenspace from multi-input image sequences

The paper presents a real time gesture recognition method for interactive systems which can perform an interaction like human communication. This method recognizes a user's gesture using the eigenspace constructed from multi-input image sequences. Since this eigenspace represents the approximate 3D information of gesture, complicated gestures which may have self-occlusion and confusion in a single input image sequence can be recognized correctly. Moreover, the method is suitable for obtaining the degree information of gesture, such as speed and magnitude. Using the method, the authors realize a real time interactive system, the Virtual Conductor System, which can control music played by a computer using gesture recognition results, and indicates the usefulness of the method.

Real Time Gesture Recognition in A Real Complex Environment Using Interactive Model Generation

This paper describes a real time gesture recognition method for interactive systems. Our method can recognize gesture robustly in a variety of real complex environments. For the robust gesture recognition, our method performs an interactive model generation before the recognition processing, and an individual gesture model is constructed by the interaction between a user and the computer. The individual gesture model is based on the template matching technique and it is constituted by the set of color template images which represent a set of specific poses of human part in a gesture. Using our method, we realize a real time interactive system, the Gesture Game System, which can activate the characters of video game by gestures in real-time and indicates the usefulness of our method.