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In the pdf version of this article, the captured corresponding author was incorrect when it is correctly presented in the online version. The correct corresponding author should be Hadi Seyedarabi. It is corrected in this correction article. © 2018, Springer Science+Business Media, LLC, part of Springer Nature  

In the pdf version of this article, the captured corresponding author was incorrect when it is correctly presented in the online version. The correct corresponding author should be Hadi Seyedarabi. It is corrected in this correction article. © 2018, Springer Science+Business Media, LLC, part of Springer Nature  

In the pdf version of this article, the captured corresponding author was incorrect when it is correctly presented in the online version. The correct corresponding author should be Hadi Seyedarabi. It is corrected in this correction article. © 2018, Springer Science+Business Media, LLC, part of Springer Nature  

Fully automatic algorithm for estimating the 3D human pose from multiple uncalibrated cameras is presented. Unlike the state-of-the-art methods which use the estimated pose of previous frames to restrict the candidates of current frame, the proposed method uses the viewpoint of previous frame in order to obtain an accurate pose. This paper also introduces a method to incorporate pose estimation results of several cameras without using the calibration information. The algorithm employs a rich descriptor for matching purposes. The performance of the proposed method is evaluated on a soccer database which is captured by multiple cameras. The dataset of silhouettes, in which the related 3D... 

Multiple human 3D pose estimation is a useful but challenging task in computer vison applications. The ambiguities in estimation of 2D and 3D poses of multiple persons can be verified by using multi-view frames, in which the occluded or self-occluded body parts of some persons might be visible in other camera views. But, when cameras are moving and uncalibrated, estimating the association of multiple human body parts among different camera views is a challenging task. This paper presents novel methods for multiple human 3D pose estimation and pose association in multi-view camera frames in an uncalibrated camera setup using an adversarial learning framework. The generator is a 3D pose... 

Multiple human 3D pose estimation is a useful but challenging task in computer vison applications. The ambiguities in estimation of 2D and 3D poses of multiple persons can be verified by using multi-view frames, in which the occluded or self-occluded body parts of some persons might be visible in other camera views. But, when cameras are moving and uncalibrated, estimating the association of multiple human body parts among different camera views is a challenging task. This paper presents novel methods for multiple human 3D pose estimation and pose association in multi-view camera frames in an uncalibrated camera setup using an adversarial learning framework. The generator is a 3D pose...