3D Hand Pose Reconstruction Using Specialized Mappings

Rosales, Rómer; Athitsos, Vassilis; Sigal, Leonid; Sclaroff, Stan

doi:10.21236/ada451286

Cited by 90 publications

(44 citation statements)

References 28 publications

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“…In this implementation, we are not considering hand occlusion analysis, which by itself is a difficult task. Our system tracks both hands of the user automatically using a skin color tracker (Sigal et al, 2000;Rosales et al, 2001).…”

Section: Hand Detection and Segmentationmentioning

confidence: 99%

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

Rosales

Sclaroff

2006

Int J Comput Vision

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We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation; the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body.Keywords: human body pose, hand pose, nonrigid and articulated pose estimation, statistical inference, generative and discriminative models, mixture models, expectation maximization algorithm

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Section: Hand Detection and Segmentationmentioning

confidence: 99%

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

Rosales

Sclaroff

2006

Int J Comput Vision

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“…Appearance-based methods employ an offline training process for establishing a mapping from a set of image features to a finite set of hand model configurations [3,[18][19][20]23]. The discriminative power of these methods depends on the invariance properties of the employed features, the number and the diversity of the training postures and the method used to derive the mapping.…”

Section: Related Workmentioning

confidence: 99%

Tracking the articulated motion of two strongly interacting hands

Oikonomidis

Kyriazis

Argyros

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

212

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We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this problem, we employ Particle Swarm Optimization (PSO), an evolutionary, stochastic optimization method with the objective of finding the two-hands configuration that best explains observations provided by an RGB-D sensor. To the best of our knowledge, the proposed method is the first to attempt and achieve the articulated motion tracking of two strongly interacting hands. Extensive quantitative and qualitative experiments with simulated and real world image sequences demonstrate that an accurate and efficient solution of this problem is indeed feasible.

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“…For the so called appearance-based approaches [2,21,27,20,10], a large set of hand configurations is generated off-line. Relevant features are extracted for each of the generated poses, resulting in a database where each pose is associated with image features.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Quasi-Random Search for Hand Articulations Tracking

Oikonomidis

Lourakis

Argyros

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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We present a new method for tracking the 3D position, global orientation and full articulation of human hands. Inspired by recent advances in model-based, hypothesizeand-test methods, the high-dimensional parameter space of hand configurations is explored with a novel evolutionary optimization technique. The proposed method capitalizes on the fact that the quasi-random samples of the Sobol sequence have low discrepancy and exhibit a more uniform coverage of the sampled space compared to random samples obtained from the uniform distribution. The method has been tested for the problems of tracking the articulation of a single hand (27D parameter space) and two hands (54D space). Extensive experiments have been carried out with synthetic and real data, in comparison with state of the art methods. The quantitative evaluation shows that for cases of limited computational resources, the new approach achieves a speed-up of four (single hand tracking) and eight (two hands tracking) without compromising tracking accuracy. Interestingly, the proposed method is preferable compared to the state of the art either in the case of limited computational resources or in the case of more complex (i.e., higher dimensional) problems, thus improving the applicability of the method in a number of application domains.

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3D Hand Pose Reconstruction Using Specialized Mappings

Abstract: A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA)

Cited by 90 publications

References 28 publications

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

Tracking the articulated motion of two strongly interacting hands

Evolutionary Quasi-Random Search for Hand Articulations Tracking

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