A Study on the Learning Based Human Pose Recognition

Human pose recognition is considered a well-known process of estimating the human body pose from a single image or a series of video frames. There exist many applications that can benefit from human pose technology e.g. activity recognition, human tracking, 3D gaming, character animation, clinical analysis of human gait and other HCI applications. Due to its many challenges, such as illumination, occlusion, outdoor environment and clothing, it is considered one of the active areas in computer vision. For the last 15 years, Human pose recognition problem significantly gained interest of many researchers and therefore, many techniques were proposed in order to address the challenges of human pose recognition. In this study, we review the recently progressed work in human pose recognition using computer vision feature extraction and machine learning classification techniques. Accordingly, we identify gaps in existing work and give direction for future work.

[1]  Guillaume-Alexandre Bilodeau,et al.  A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm , 2010, Sensors.

[2]  Jordi Gonzàlez,et al.  Human Pose Estimation from Monocular Images: A Comprehensive Survey , 2016, Sensors.

[3]  Hong Wei,et al.  A survey of human motion analysis using depth imagery , 2013, Pattern Recognit. Lett..

[4]  Guodong Guo,et al.  A survey on still image based human action recognition , 2014, Pattern Recognit..

[5]  Fiora Pirri,et al.  Bayesian Image Based 3D Pose Estimation , 2016, ECCV.

[6]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[7]  Rémi Ronfard,et al.  A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..

[8]  Christian Wolf,et al.  Human body part estimation from depth images via spatially-constrained deep learning , 2014, Pattern Recognition Letters.

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

[10]  Yi Li,et al.  Beyond Physical Connections: Tree Models in Human Pose Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  R. Z. Hu.  Vision-Based Observation Models for Lower Limb 3D Tracking with a Moving Platform , 2011 .

[12]  Richard Bowden,et al.  Putting the pieces together: Connected Poselets for human pose estimation , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[13]  S.J. Krotosky,et al.  Occupant posture analysis using reflectance and stereo image for "smart" airbag deployment , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[14]  Mohan M. Trivedi,et al.  Human Pose Estimation and Activity Recognition From Multi-View Videos: Comparative Explorations of Recent Developments , 2012, IEEE Journal of Selected Topics in Signal Processing.

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

[16]  Sebastian Thrun,et al.  Real time motion capture using a single time-of-flight camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[18]  Sergio Escalera,et al.  A Survey on Model Based Approaches for 2D and 3D Visual Human Pose Recovery , 2014, Sensors.

[19]  Thomas Helten,et al.  Processing and tracking human motions using optical, inertial, and depth sensors , 2013 .

[20]  Chun Chen,et al.  A survey of human pose estimation: The body parts parsing based methods , 2015, J. Vis. Commun. Image Represent..

[21]  A. Fathi,et al.  Human Pose Estimation using Motion Exemplars , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  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.

[23]  Ao Li,et al.  A novel method for user-defined human posture recognition using Kinect , 2014, 2014 7th International Congress on Image and Signal Processing.

[24]  Choubik Youness,et al.  Machine Learning for Real Time Poses Classification Using Kinect Skeleton Data , 2016, CGiV 2016.

[25]  Cristian Sminchisescu.  3D Human Motion Analysis in Monocular Video Techniques and Challenges , 2006, AVSS.

[26]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Larry S. Davis,et al.  A Robust Background Subtraction and Shadow Detection , 1999 .

[28]  Wenbing Zhao,et al.  A Survey of Applications and Human Motion Recognition with Microsoft Kinect , 2015, Int. J. Pattern Recognit. Artif. Intell..

[29]  Jean-Marc Lavest,et al.  Background subtraction adapted to PTZ cameras by keypoint density estimation , 2010, BMVC.

[30]  Yi Li,et al.  Vision-based human pose estimation for pervasive computing , 2009, AMC '09.

[31]  Silvio Savarese,et al.  Articulated part-based model for joint object detection and pose estimation , 2011, 2011 International Conference on Computer Vision.

[32]  Andrew Zisserman,et al.  2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images , 2012, International Journal of Computer Vision.

[33]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[34]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[35]  Gérard G. Medioni,et al.  Human pose estimation from a single view point, real-time range sensor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[36]  Bernt Schiele,et al.  A database for fine grained activity detection of cooking activities , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[38]  Conrad S. Tucker,et al.  Machine learning classification of design team members' body language patterns for real time emotional state detection , 2015 .

[39]  R. Birdwhistell.  Kinesics and Context: Essays on Body Motion Communication , 1971 .

[40]  Y. Trope,et al.  Body Cues, Not Facial Expressions, Discriminate Between Intense Positive and Negative Emotions , 2012, Science.

[41]  Ioannis A. Kakadiaris,et al.  3D Human pose estimation: A review of the literature and analysis of covariates , 2016, Comput. Vis. Image Underst..

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

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

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

[45]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).