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Abstract: Articulated hand tracking systems have been commonly used in virtual reality applications, including selection–move–release systems that use freehand as input device. However, building an effective real-time hand tracker remains a challenge. Motivated by the need for three-dimensional (3D) freehand-based human–computer interface in the selection–move–release systems, the current study aims to develop 3D freehand tracking, in which the high dimensionality of articulated hand models is one of the dominating obstacles for tracking 3D hand in real-time. This study focuses on forming the behavioral model for users in selection–move–release systems to build a natural, direct, and effective human–computer interface. First, the statistical models for users were learned. Second, the behavioral models were derived from the statistical models to form a general behavioral model database. Third, a freehand tracking algorithm for interaction between human and computer was presented based on the behavioral models. Fourth, the proposed approach was tested in several real selection–move–release systems, and the experimental results were provided. Unlike previous studies on this subject, the proposed behavioral model was composed of Correlation among Local Motion Models and cognitive information. Experimental results show that the proposed algorithm can achieve satisfactory results in relation to time cost and accuracy compared with some previous studies. [Copyright &y& Elsevier]

Abstract: Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed. [ABSTRACT FROM AUTHOR]