2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities [1]. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.

[1]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[2]  Andrew Zisserman,et al.  Progressive search space reduction for human pose estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Vittorio Ferrari,et al.  Better Appearance Models for Pictorial Structures , 2009, BMVC.

[4]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion , 2010, International Journal of Computer Vision.

[5]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[6]  Vittorio Ferrari,et al.  We Are Family: Joint Pose Estimation of Multiple Persons , 2010, ECCV.

[7]  Fei-Fei Li,et al.  Modeling mutual context of object and human pose in human-object interaction activities , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Mark Everingham,et al.  Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation , 2010, BMVC.

[9]  Yang Wang,et al.  Learning hierarchical poselets for human parsing , 2011, CVPR 2011.

[10]  David R Bassett,et al.  2011 Compendium of Physical Activities: a second update of codes and MET values. , 2011, Medicine and science in sports and exercise.

[11]  Mark Everingham,et al.  Learning effective human pose estimation from inaccurate annotation , 2011, CVPR 2011.

[12]  Ben Taskar,et al.  Parsing human motion with stretchable models , 2011, CVPR 2011.

[13]  Subhransu Maji.  Large Scale Image Annotations on Amazon Mechanical Turk , 2011 .

[14]  Deva Ramanan,et al.  Efficiently Scaling up Crowdsourced Video Annotation , 2012, International Journal of Computer Vision.

[15]  Vittorio Ferrari,et al.  Human Pose Co-Estimation and Applications , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Peter V. Gehler,et al.  Strong Appearance and Expressive Spatial Models for Human Pose Estimation , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Ben Taskar,et al.  MODEC: Multimodal Decomposable Models for Human Pose Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Jitendra Malik,et al.  Articulated Pose Estimation Using Discriminative Armlet Classifiers , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Cordelia Schmid,et al.  Towards Understanding Action Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[20]  Luc Van Gool,et al.  Human Pose Estimation Using Body Parts Dependent Joint Regressors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Yi Yang,et al.  Articulated Human Detection with Flexible Mixtures of Parts , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Bernt Schiele,et al.  Fine-Grained Activity Recognition with Holistic and Pose Based Features , 2014, GCPR.

[23]  Cristian Sminchisescu,et al.  Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.