Poselet Conditioned Pictorial Structures

Pishchulin, Leonid; Andriluka, Mykhaylo; Gehler, Peter V.; Schiele, Bernt

doi:10.1109/cvpr.2013.82

Cited by 317 publications

(196 citation statements)

References 25 publications

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“…The images are of people in various sport poses. We use the observercentric annotations as used in [10] for training and testing. We train a full body model comprised of a 2-level hierarchy.…”

Section: Discussionmentioning

confidence: 99%

“…Recent work [11,10] has shown that part detections conditioned on the location of larger composite parts improves pose estimation performance; however, these composite parts are often constructed to form tree graph structures [16]. Inspired by these recent advances, we design a hierarchical inference machine that similarly encodes these interactions among parts at different scales in the image.…”

Section: Incorporating a Hierarchymentioning

confidence: 99%

“…Performing exact inference on these models is typically intractable and approximate methods at learning and test time need to be used. Recent methods have also explored using part hierarchies [16,17] and condition the detection of smaller parts that model regions around anatomical joints on the localization of larger composite parts or poselets [11,10,18,19] that model limbs in canonical configurations and tend to be easier to detect.…”

Section: Related Workmentioning

confidence: 99%

“…Third, its modular architecture allows the use of high capacity predictors which are better suited to deal with the highly multi-modal appearance of each part. Inspired by recent work [10,11] that has demonstrated the importance of conditioning finer part detection on the detection of larger composite parts in order to improve localization, we incorporate these multi-scale cues in our framework by also modeling a hierarchy of parts.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Pose Machines: Articulated Pose Estimation via Inference Machines

Ramakrishna

Munoz

Hebert

et al. 2014

Lecture Notes in Computer Science

204

131

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Abstract. State-of-the-art approaches for articulated human pose estimation are rooted in parts-based graphical models. These models are often restricted to tree-structured representations and simple parametric potentials in order to enable tractable inference. However, these simple dependencies fail to capture all the interactions between body parts. While models with more complex interactions can be defined, learning the parameters of these models remains challenging with intractable or approximate inference. In this paper, instead of performing inference on a learned graphical model, we build upon the inference machine framework and present a method for articulated human pose estimation. Our approach incorporates rich spatial interactions among multiple parts and information across parts of different scales. Additionally, the modular framework of our approach enables both ease of implementation without specialized optimization solvers, and efficient inference. We analyze our approach on two challenging datasets with large pose variation and outperform the state-of-the-art on these benchmarks.

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Section: Discussionmentioning

confidence: 99%

Section: Incorporating a Hierarchymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pose Machines: Articulated Pose Estimation via Inference Machines

Ramakrishna

Munoz

Hebert

et al. 2014

Lecture Notes in Computer Science

204

131

View full text Add to dashboard Cite

show abstract

“…Furthermore, out of studio environments usually include dynamic background and clutter. To address these challenges, most of the recent work relies on modelling the human body from an ensemble of parts [2,3].…”

Section: Introductionmentioning

confidence: 99%

Holistic Human Pose Estimation with Regression Forests

Belagiannis

Amann

Navab

et al. 2014

Articulated Motion and Deformable Objects

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Abstract. In this work, we address the problem of human pose estimation in still images by proposing a holistic model for learning the appearance of the human body from image patches. These patches, which are randomly chosen, are used for extracting features and training a regression forest. During training, a mapping between image features and human poses, defined by joint offsets, is learned; while during prediction, the body joints are estimated with an efficient mode-seeking algorithm. In comparison to other holistic approaches, we can recover body poses from occlusion or noisy data. We demonstrate the power of our method in two publicly available datasets and propose a third one. Finally, we achieve state-of-the-art results in comparison to other approaches.

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MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network

Kocabas

Karagoz

Akbaş

2018

Lecture Notes in Computer Science

276

128

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In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet can jointly handle person detection, keypoint detection, person segmentation and pose estimation problems. The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detections, and produces accurate poses by assigning keypoints to person instances. On the COCO keypoints dataset, our pose estimation method outperforms all previous bottom-up methods both in accuracy (+4-point mAP over previous best result) and speed; it also performs on par with the best top-down methods while being at least 4x faster. Our method is the fastest real time system with ∼23 frames/sec. Source code is available at: https://github.com/mkocabas/pose-residual-network

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Poselet Conditioned Pictorial Structures

Abstract: Abstract

Cited by 317 publications

References 25 publications

Pose Machines: Articulated Pose Estimation via Inference Machines

Pose Machines: Articulated Pose Estimation via Inference Machines

Holistic Human Pose Estimation with Regression Forests

MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network

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