3D hand pose reconstruction using specialized mappings

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

doi:10.1109/iccv.2001.937543

Cited by 116 publications

(72 citation statements)

References 26 publications

(21 reference statements)

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“…For example, our tests have shown that using a face detector [30] to bootstrap initialization, considerably boosts the performance of our system.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Skin color-based video segmentation under time-varying illumination

Sigal,

Sclaroff,

Athitsos

2004

IEEE Trans. Pattern Anal. Machine Intell.

Self Cite

224

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“…For example, our tests have shown that using a face detector [30] to bootstrap initialization, considerably boosts the performance of our system.…”

Section: Resultsmentioning

confidence: 99%

“…12) or specular highlights on the background objects. Initialization and subsequent segmentation accuracy could be further improved via the use of shape and blob-based motion constraints [7], and/or domainspecific constraints like face detection [30].…”

Section: Discussionmentioning

confidence: 99%

Skin color-based video segmentation under time-varying illumination

Sigal,

Sclaroff,

Athitsos

2004

IEEE Trans. Pattern Anal. Machine Intell.

Self Cite

224

View full text Add to dashboard Cite

show abstract

“…Early generative models were specified manually (e.g., with joint limits and smoothness constraints), while many recent generative models have been learned from motion capture data of people performing specific actions (e.g., Choo and Fleet 2001;Herda et al 2005;Pavlović et al 1999;Sidenbladh et al 2000;Sminchisescu and Jepson 2004;Urtasun et al 2006;Wachter and Nagel 1999). Discriminative models also depend strongly on human motion capture data, based on which direct mappings from image measurements to human pose and motion are learned (Agarwal and Triggs 2006;Elgammal and Lee 2004;Rosales et al 2001;Shakhnarovich et al 2003;Sminchisescu et al 2007).…”

Section: Related Workmentioning

confidence: 99%

Physics-Based Person Tracking Using the Anthropomorphic Walker

2009

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We introduce a physics-based model for 3D person tracking. Based on a biomechanical characterization of lower-body dynamics, the model captures important physical properties of bipedal locomotion such as balance and ground contact. The model generalizes naturally to variations in style due to changes in speed, step-length, and mass, and avoids common problems (such as footskate) that arise with existing trackers. The dynamics comprise a two degreeof-freedom representation of human locomotion with inelastic ground contact. A stochastic controller generates impulsive forces during the toe-off stage of walking, and springlike forces between the legs. A higher-dimensional kinematic body model is conditioned on the underlying dynamics. The combined model is used to track walking people in video, including examples with turning, occlusion, and varying gait. We also report quantitative monocular and binocular tracking results with the HumanEva dataset.

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“…The last class is further divided to appearance-based and model-based methods. Appearance-based methods [15,16,18,20] estimate hand configurations directly from images, using a precomputed mapping from the image feature space to the hand configuration. Model-based methods [2,4,5,[10][11][12] search the solution space of the problem for the hand configuration that is most compatible to the observed hand.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction for Efficient Single Frame Hand Pose Estimation

Douvantzis

Oikonomidis

Kyriazis

et al. 2013

Lecture Notes in Computer Science

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Abstract. Model based approaches for the recovery of the 3D position, orientation and full articulation of the human hand have a number of attractive properties. One bottleneck towards their practical exploitation is their computational cost. To a large extent, this is determined by the large dimensionality of the problem to be solved. In this work we exploit the fact that the parametric joints space representing hand configurations is highly redundant. Thus, we employ Principal Component Analysis (PCA) to learn a lower dimensional space that describes compactly and effectively the human hand articulation. The reduced dimensionality of the resulting space leads to a simpler optimization problem, so model-based approaches require less computational effort to solve it. Experiments demonstrate that the proposed approach achieves better accuracy in hand pose recovery compared to a state of the art baseline method using only 1/4 of the latter's computational budget.

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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 116 publications

References 26 publications

Skin color-based video segmentation under time-varying illumination

Skin color-based video segmentation under time-varying illumination

Physics-Based Person Tracking Using the Anthropomorphic Walker

Dimensionality Reduction for Efficient Single Frame Hand Pose Estimation

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