3D Pictorial Structures for Multiple View Articulated Pose Estimation

Burénius, Magnus; Sullivan, Josephine; Carlsson, Stefan

doi:10.1109/cvpr.2013.464

Cited by 152 publications

(175 citation statements)

References 13 publications

(13 reference statements)

Supporting

Mentioning

173

Contrasting

Unclassified

Order By: Relevance

“…The most notable part-based model are Pictorial Structures for 2D [4,18,20] or 3D body pose estimation [7,14]. The model has been successfully applied on multiple human pose estimation in 2D [3,17,41] and 3D [7] as well.…”

Section: Related Workmentioning

confidence: 99%

Parsing human skeletons in an operating room

Belagiannis

Wang

Shitrit

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

Multiple human pose estimation is an important yet challenging problem. In an Operating Room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localising and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures (3DPS) and 2D body part localization across all camera views, using Convolutional Neural Networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

Parsing human skeletons in an operating room

Belagiannis

Wang

Shitrit

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…We use the joint detector from [2] and learn the body prior using the ground-truth data from the KTH Multiview Football II dataset [9]. The evaluation is divided into three tasks: analysis of the state space, evaluation on the human detection and the body pose estimation.…”

Section: Methodsmentioning

confidence: 99%

“…Pictorial structures is the most common part-based model for estimating the 2D body pose of single human [3,11,13]. The model has been extended to the 3D space, in order to cope with mutli-view camera setups as well [2,9,16]. Recently, pictorial structures have been successfully modelled for multiple human 3D pose estimation [6].…”

Section: Introductionmentioning

confidence: 99%

Multiple Human Pose Estimation with Temporally Consistent 3D Pictorial Structures

Belagiannis

Wang

Schiele

et al. 2015

Computer Vision - ECCV 2014 Workshops

View full text Add to dashboard Cite

Abstract. Multiple human 3D pose estimation from multiple camera views is a challenging task in unconstrained environments. Each individual has to be matched across each view and then the body pose has to be estimated. Additionally, the body pose of every individual changes in a consistent manner over time. To address these challenges, we propose a temporally consistent 3D Pictorial Structures model (3DPS) for multiple human pose estimation from multiple camera views. Our model builds on the 3D Pictorial Structures to introduce the notion of temporal consistency between the inferred body poses. We derive this property by relying on multi-view human tracking. Identifying each individual before inference significantly reduces the size of the state space and positively influences the performance as well. To evaluate our method, we use two challenging multiple human datasets in unconstrained environments. We compare our method with the state-of-the-art approaches and achieve better results.

show abstract

“…One of the key challenges in that setting is that the appearance of people is more diverse, and simple means of representing observations based on background subtraction are not applicable due to the presence of multiple people and interactions between people and scene objects. Inspired by recent results in 2D pose estimation [3,18], several approaches have proposed to build upon and adapt these results for pose estimation in 3D [1,4,5,12]. In these approaches 2D detectors are either used to model the likelihood of the 3D pose [5,12], or provide a set of proposals for positions of body joints that are subsequently refined by reasoning in 3D [1,4].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…In this paper we consider the task of articulated 3D human pose estimation in challenging scenes with dynamic background and multiple people. Initial progress on this task has been achieved building on discriminatively trained part-based models that deliver a set of 2D body pose candidates that are then subsequently refined by reasoning in 3D [1,4,5]. The performance of such methods is limited by the performance of the underlying 2D pose estimation approaches.…”

mentioning

confidence: 99%

Test-Time Adaptation for 3D Human Pose Estimation

Amin

Müller

Bulling

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper we consider the task of articulated 3D human pose estimation in challenging scenes with dynamic background and multiple people. Initial progress on this task has been achieved building on discriminatively trained part-based models that deliver a set of 2D body pose candidates that are then subsequently refined by reasoning in 3D [1,4,5]. The performance of such methods is limited by the performance of the underlying 2D pose estimation approaches. In this paper we explore a way to boost the performance of 2D pose estimation based on the output of the 3D pose reconstruction process, thus closing the loop in the pose estimation pipeline. We build our approach around a component that is able to identify true positive pose estimation hypotheses with high confidence. We then either retrain 2D pose estimation models using such highly confident hypotheses as additional training examples, or we use similarity to these hypotheses as a cue for 2D pose estimation. We consider a number of features that can be used for assessing the confidence of the pose estimation results. The strongest feature in our comparison corresponds to the ensemble agreement on the 3D pose output. We evaluate our approach on two publicly available datasets improving over state of the art in each case.

show abstract

3D Pictorial Structures for Multiple View Articulated Pose Estimation

Cited by 152 publications

References 13 publications

Parsing human skeletons in an operating room

Parsing human skeletons in an operating room

Multiple Human Pose Estimation with Temporally Consistent 3D Pictorial Structures

Test-Time Adaptation for 3D Human Pose Estimation

Contact Info

Product

Resources

About