MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion (This dataset can be downloaded from http://cs.nyu.edu/~ajain/accv2014/.), that extends the FLIC dataset [1] with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.

[1]  G. Johansson.  Visual perception of biological motion and a model for its analysis , 1973 .

[2]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.

[3]  David C. Hogg.  Model-based vision: a program to see a walking person , 1983, Image Vis. Comput..

[4]  William T. Freeman,et al.  Orientation Histograms for Hand Gesture Recognition , 1995 .

[5]  Takeo Kanade,et al.  Model-based tracking of self-occluding articulated objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  Ioannis A. Kakadiaris,et al.  Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[10]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Cristian Sminchisescu,et al.  Monocular tracking of the human arm in 3D , 1995, Proceedings of IEEE International Conference on Computer Vision.

[12]  Jitendra Malik,et al.  Estimating Human Body Configurations Using Shape Context Matching , 2002, ECCV.

[13]  Trevor Darrell,et al.  Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  Trevor Darrell,et al.  Inferring 3D structure with a statistical image-based shape model , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[16]  Michael Isard,et al.  Tracking loose-limbed people , 2004, CVPR 2004.

[17]  David A. Forsyth,et al.  Strike a pose: tracking people by finding stylized poses , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Sebastian Thrun,et al.  SCAPE: shape completion and animation of people , 2005, SIGGRAPH 2005.

[19]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Ivan Laptev,et al.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

[21]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[22]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[24]  Hans-Peter Seidel,et al.  Performance capture from sparse multi-view video , 2008, ACM Trans. Graph..

[25]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[28]  Jitendra Malik,et al.  Poselets: Body part detectors trained using 3D human pose annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[29]  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.

[30]  Andrew Zisserman,et al.  Learning sign language by watching TV (using weakly aligned subtitles) , 2009, CVPR.

[31]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, CVPR.

[32]  Ben Taskar,et al.  Sidestepping Intractable Inference with Structured Ensemble Cascades , 2010, NIPS.

[33]  Ben Taskar,et al.  Cascaded Models for Articulated Pose Estimation , 2010, ECCV.

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

[35]  Hans-Peter Seidel,et al.  MovieReshape: tracking and reshaping of humans in videos , 2010, ACM Trans. Graph..

[36]  Hans-Peter Seidel,et al.  Performance Capture from Multi-View Video , 2010, Image and Geometry Processing for 3-D Cinematography.

[37]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

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

[39]  Hans-Peter Seidel,et al.  Fast articulated motion tracking using a sums of Gaussians body model , 2011, 2011 International Conference on Computer Vision.

[40]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[41]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

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

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

[44]  Li Deng,et al.  A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[45]  Cordelia Schmid,et al.  DeepFlow: Large Displacement Optical Flow with Deep Matching , 2013, 2013 IEEE International Conference on Computer Vision.

[46]  Luca Maria Gambardella,et al.  Fast image scanning with deep max-pooling convolutional neural networks , 2013, 2013 IEEE International Conference on Image Processing.

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

[48]  Yann LeCun,et al.  Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[50]  Peter V. Gehler,et al.  Poselet Conditioned Pictorial Structures , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[51]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[52]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Neural Networks , 2013 .

[53]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[54]  Christian Szegedy,et al.  DeepPose: Human Pose Estimation via Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[55]  Jonathan Tompson,et al.  Learning Human Pose Estimation Features with Convolutional Networks , 2013, ICLR.

[56]  R. Fergus,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[57]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[58]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[59]  Ken Perlin,et al.  Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..