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Journal Article
Bimanual Hand Tracking based on AR-KLT
- Authors
- Hye-Jin Kim, Keun-Chang Kwak, Jae Jeon Lee
- Issue Date
- 2007-06
- Citation
- Scene Reconstruction Pose Estimation and Tracking, pp.351-258
- ISSN
- ISSN없음
- Language
- English
- Type
- Journal Article
- Abstract
- Robot and human interaction has received a significant amount of attention in the robot vision research community in the past decades. This has been motivated by the desire of understanding human gesture/motion tracking and recognition. If you solve tracking problems under the circumstance of fast movement, occlusion, and illumination, then you need to complicate calculation, and hence the computational complex prevents to work in real time. For example, particle filter is an useful algorithm to track objects, even under occlusion and non-rigid motion difference. However, particle filter needs to enough samples to support reliability of the potential candidates of the target. There have done many works in hand tracking. To track hands in real time, Shan(Shan, 2004) made particle filter faster by reducing sample size according to mean shift. On the other hand, Kolsch ( Kolsch&Turk, 2004) designed a fast tracking algorithm that combined Kanade-Lucas-Tomasi(KLT) flocks and k-nearest neighborhood. Some papers concentrated on the particular properties of hands and their features. Nonrigidity of the hand causes difficulties to track because of non-linear dynamics of the articulation. Fei and Reid(Fei&Reid, 2003) dealt with deformation of the hand by constructing two models according to non-rigid motion from rigid motion. HLAC (Higher-Order Local Auto-Correlation) features of Ishihara (Ishihara&Otsu, 2004) achieved efficient information over time domain by Auto-Regressive model. The size of interesting objects is another critical factor for tracking because if its size is too small or changes too fast, object tracking becomes very challenging problem. Francçis (Francçis,2004) dealt with blobs varying their resolution, hence made it possible to track the object with various size in the image sequence. Both hands tracking is simultaneously different from one hand tracking since features such as shape, color etc. between both hands is almost the same each other. Shamaie (Shamaie&Sutherland, 2003) built the model of the movements of bimanual limbs. However, the model needs large enough time to compute distance transform in the image. McAllister( McKenna et al., 2002) solved the both hands tracking by employing contour distance transform and 2D geometric model. In this paper, we propose a new 2D both hands tracking algorithm based on the articulated structure of human body in real time. This method is efficient enough to perform in real time due to the limb model tracking. The model enables to deal with the deformation of hands and nonrigid motion because of the articulate structure of the arm for both hands. The model can be tracked by a linear line obtained from the regression of KLT features in order to represent the target information. Unlike Shamaie and McAllister, the proposed algorithm outperforms previous method in occlusion handling of both hands. For instance, some methods require restricting occlusion cases because similar features prevent a hand to differentiate from another. However, this method tracks superimposed hands correctly by virtue of its prediction of the moving direction. In the next section, we will elaborate our proposed algorithm step by step. In the section 2 A-B, we will illustrate key algorithms to build our model. In Section 2.3, we give brief explanation about how to segment and extract hands from the background. The section 2.4 is dedicated to the dynamic model and the algorithms for occlusion detection and tracking. Some experimental results are presented in the section 3. Our contribution in hand tracking and conclusion are presented at the end of paper.
- KSP Keywords
- Auto-Correlation, Autoregressive models, Both hands, Critical factors, Detection and tracking, Dynamic Models, Geometric Model, Hands Tracking, Higher order, Human body, Image sequence
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC SA)
(CC BY NC SA)
