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191
Real-time human pose recognition in parts from single depth images
- IN CVPR
, 2011
"... We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler p ..."
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Cited by 568 (17 self)
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We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.
Nonparametric Belief Propagation
- IN CVPR
, 2002
"... In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, non--Gaussian distributions. However, due to the limitations of existing inf#6F6F3 algorithms, it is of#]k necessary tof#3# coarse, ..."
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Cited by 279 (25 self)
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In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, non--Gaussian distributions. However, due to the limitations of existing inf#6F6F3 algorithms, it is of#]k necessary tof#3# coarse, discrete approximations to such models. In this paper, we develop a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernel--based approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potentialf#l3]k[[z3 allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.
Progressive search space reduction for human pose estimation
- In CVPR
, 2008
"... The objective of this paper is to estimate 2D human pose as a spatial configuration of body parts in TV and movie video shots. Such video material is uncontrolled and extremely challenging. We propose an approach that progressively reduces the search space for body parts, to greatly improve the chan ..."
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Cited by 226 (30 self)
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The objective of this paper is to estimate 2D human pose as a spatial configuration of body parts in TV and movie video shots. Such video material is uncontrolled and extremely challenging. We propose an approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed. This involves two contributions: (i) a generic detector using a weak model of pose to substantially reduce the full pose search space; and (ii) employing ‘grabcut ’ initialized on detected regions proposed by the weak model, to further prune the search space. Moreover, we also propose (iii) an integrated spatiotemporal model covering multiple frames to refine pose estimates from individual frames, with inference using belief propagation. The method is fully automatic and self-initializing, and explains the spatio-temporal volume covered by a person moving in a shot, by soft-labeling every pixel as belonging to a particular body part or to the background. We demonstrate upper-body pose estimation by an extensive evaluation over 70000 frames from four episodes of the TV series Buffy the vampire slayer, and present an application to fullbody action recognition on the Weizmann dataset. 1.
ZISSERMAN A.: Tracking people by learning their appearance.
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
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Discriminative Density Propagation for 3D Human Motion Estimation
- In CVPR
, 2005
"... We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a predi ..."
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Cited by 114 (16 self)
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We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a predictive observation model. Instead, it uses a large human motion capture data-base and a 3D computer graphics human model in order to synthesize training pairs of typical human configurations together with their realistically rendered 2D silhouettes. These are used to directly learn to predict the conditional state distributions required for 3D body pose tracking and thus avoid using the generative 3D model for inference (the learned discriminative predictors can also be used, complementary, as importance samplers in order to improve mixing or initialize generative inference algorithms). We aim for probabilistically motivated tracking algorithms and for models that can represent complex multivalued mappings common in inverse, uncertain perception inferences. Our paper has three contributions: (1) we establish the density propagation rules for discriminative inference in continuous, temporal chain models; (2) we propose flexible algorithms for learning multimodal state distributions based on compact, conditional Bayesian mixture of experts models; and (3) we demonstrate the algorithms empirically on real and motion capture-based test sequences and compare against nearest-neighbor and regression methods.
Guiding Model Search Using Segmentation
, 2005
"... ... paradigm can be used to improve the efficiency and accuracy of model search in an image. We operationalize this idea using an over-segmentation of an image into superpixels. The problem domain we explore is human body pose estimation from still images. The superpixels prove useful in two ways. F ..."
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Cited by 84 (0 self)
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... paradigm can be used to improve the efficiency and accuracy of model search in an image. We operationalize this idea using an over-segmentation of an image into superpixels. The problem domain we explore is human body pose estimation from still images. The superpixels prove useful in two ways. First, we restrict the joint positions in our human body model to lie at centers of superpixels, which reduces the size of the model search space. In addition, accurate support masks for computing features on half-limbs of the body model are obtained by using agglomerations of superpixels as half-limb segments. We present results on a challenging dataset of people in sports news images.
Measure locally, reason globally: Occlusion-sensitive articulated pose estimation
- In CVPR 2006
, 2006
"... Part-based tree-structured models have been widely used for 2D articulated human pose-estimation. These approaches admit efficient inference algorithms while capturing the important kinematic constraints of the human body as a graphical model. These methods often fail however when multiple body part ..."
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Cited by 83 (3 self)
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Part-based tree-structured models have been widely used for 2D articulated human pose-estimation. These approaches admit efficient inference algorithms while capturing the important kinematic constraints of the human body as a graphical model. These methods often fail however when multiple body parts fit the same image region resulting in global pose estimates that poorly explain the overall image evidence. Attempts to solve this problem have focused on the use of strong prior models that are limited to learned activities such as walking. We argue that the problem actually lies with the image observations and not with the prior. In particular, image evidence for each body part is estimated independently of other parts without regard to self-occlusion. To address this we introduce occlusion-sensitive local likelihoods that approximate the global image likelihood using per-pixel hidden binary variables that encode the occlusion relationships between parts. This occlusion reasoning introduces interactions between non-adjacent body parts creating loops in the underlying graphical model. We deal with this using an extension of an approximate belief propagation algorithm (PAMPAS). The algorithm recovers the real-valued 2D pose of the body in the presence of occlusions, does not require strong priors over body pose and does a quantitatively better job of explaining image evidence than previous methods. 1.
Real time motion capture using a single time-of-flight camera
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2010
"... Markerless tracking of human pose is a hard yet relevant problem. In this paper, we derive an efficient filtering algorithm for tracking human pose using a stream of monocular depth images. The key idea is to combine an accurate generative model—which is achievable in this setting using programmable ..."
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Cited by 66 (2 self)
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Markerless tracking of human pose is a hard yet relevant problem. In this paper, we derive an efficient filtering algorithm for tracking human pose using a stream of monocular depth images. The key idea is to combine an accurate generative model—which is achievable in this setting using programmable graphics hardware—with a discriminative model that provides data-driven evidence about body part locations. In each filter iteration, we apply a form of local model-based search that exploits the nature of the kinematic chain. As fast movements and occlusion can disrupt the local search, we utilize a set of discriminatively trained patch classifiers to detect body parts. We describe a novel algorithm for propagating this noisy evidence about body part locations up the kinematic chain using the unscented transform. The resulting distribution of body configurations allows us to reinitialize the model-based search. We provide extensive experimental results on 28 real-world sequences using automatic ground-truth annotations from a commercial motion capture system. 1.
Approximate Bayesian Multibody Tracking
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Visual tracking of multiple targets is a challenging problem, especially when efficiency is an issue. Occlusions, if not properly handled, are a major source of failure. Solutions supporting prin-cipled occlusion reasoning have been proposed but are yet unpractical for online applications. This pape ..."
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Cited by 65 (8 self)
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Visual tracking of multiple targets is a challenging problem, especially when efficiency is an issue. Occlusions, if not properly handled, are a major source of failure. Solutions supporting prin-cipled occlusion reasoning have been proposed but are yet unpractical for online applications. This paper presents a new solution which effectively manages the trade-off between reliable modeling and computational efficiency. The Hybrid Joint-Separable (HJS) filter is derived from a joint Bayesian formulation of the problem, and shown to be efficient while optimal in terms of compact belief representation. Computational efficiency is achieved by employing a Markov random field approximation to joint dynamics and an incremental algorithm for posterior update with an appearance likelihood that implements a physically-based model of the occlusion process. A particle filter implementation is proposed which achieves accurate tracking during partial occlusions, while in case of complete occlusion tracking hypotheses are bound to estimated occlusion volumes. Experiments show that the proposed algorithm is efficient, robust and able to resolve long term occlusions between targets with identical appearance.
