Multiple Human Pose Estimation with Temporally Consistent 3D Pictorial Structures

Belagiannis, Vasileios; Wang, Xinchao; Schiele, Bernt; Fua, Pascal; Ilić, Slobodan; Navab, Nassir

doi:10.1007/978-3-319-16178-5_52

Cited by 37 publications

(36 citation statements)

References 27 publications

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“…The following three datasets are used for evaluation: Campus [1]: It is a dataset consisting of three people interacting with each other in an outdoor environment, captured with three calibrated cameras. We follow the same evaluation protocol as in previous works [1,3,2,12] and use the percentage of correctly estimated parts (PCP) to measure the accuracy of 3D location of the body parts. Shelf [1]: Compared with Campus, this dataset is more complex, which consists of four people disassembling a shelf at a close range.…”

Section: Datasetsmentioning

confidence: 99%

“…We compare with the following baseline methods. Belagiannis et al [1,3] were among the first to introduce 3DPS model-based multi-person pose estimation and was extended to the video case to leverage temporal consistency [2]. Ershadi-Nasab et al [12] is a very recent method that proposes to cluster the 3D candidate joints to reduce the state space.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

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Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views

Dong

Wen

Huang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

172

176

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This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. The proposed approach achieves significant performance gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the Campus and Shelf datasets, respectively), while being efficient for real-time applications.

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Section: Datasetsmentioning

confidence: 99%

Section: Comparison With State-of-the-artmentioning

confidence: 99%

Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views

Dong

Wen

Huang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

172

176

View full text Add to dashboard Cite

show abstract

“…The concept of temporal consistency is added to the 3D Pictorial Structure model in [3] to address the problem of multiple human pose estimation from multiple camera viewpoints.…”

Section: Related Workmentioning

confidence: 99%

3D human pose estimation from depth maps using a deep combination of poses

Marín-Jiménez

Romero-Ramirez

Muñoz-Salinas

et al. 2018

Journal of Visual Communication and Image Representation

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Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.

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“…Current multi-view human pose estimation approaches have been proposed either for single-person scenarios [14,8,18,2] or for multi-person scenarios in which the number of persons is known in advance [25,6,7]. The approach proposed in this work makes no assumption about the number of persons in the scene.…”

Section: Introductionmentioning

confidence: 99%

A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms

Kadkhodamohammadi

Gangi

Mathelin

et al. 2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Many approaches have been proposed for human pose estimation in single and multi-view RGB images. However, some environments, such as the operating room, are still very challenging for state-of-the-art RGB methods. In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features. The proposed method permits the joint detection and estimation of the poses without knowing a priori the number of persons present in the scene. We evaluate this approach on a novel multi-view RGB-D dataset acquired during live surgeries and annotated with ground truth 3D poses.

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Multiple Human Pose Estimation with Temporally Consistent 3D Pictorial Structures

Cited by 37 publications

References 27 publications

Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views

Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views

3D human pose estimation from depth maps using a deep combination of poses

A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms

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