Recognizing Constrained 3D Human Motion: An Inference Approach

Enormous uncertainties in unconstrained human motions lead to a fundamental challenge that many recognising algorithms have to face in practice: motion recognition has to be efficiently correct, but verifying whether or not the algorithm is robustly following the true target motion tends to be demanding, especially when human kinematic motions heavily overlap and occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive or efficient but vulnerable to false alarms. This chapter presents a novel inference engine for recognising occluded 3D human motion assisted by the recognition context. First, uncertainties are wrapped into a fuzzy membership function via a novel method called fuzzy quantile generation which employs metrics derived from the probabilistic quantile function. Then, time-dependent and context-aware rules are produced via a genetic programming to smooth the qualitative outputs represented by fuzzy membership functions. Finally, occlusion in motion recognition is taken care of by introducing new procedures for feature selection and feature reconstruction. Experimental results demonstrate the effectiveness of the proposed inference engine for 3D occluded human motion recognition.

[1]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[2]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Narendra Ahuja,et al.  Extraction and classification of visual motion patterns for hand gesture recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[4]  Witold Pedrycz,et al.  Fuzzy modelling through logic optimization , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[5]  Tieniu Tan,et al.  Agent orientated annotation in model based visual surveillance , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[6]  Manuela M. Veloso,et al.  Conditional random fields for activity recognition , 2007, AAMAS '07.

[7]  Gang Qian,et al.  Dance posture recognition using wide-baseline orthogonal stereo cameras , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[8]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Hartmut Witte,et al.  ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion--part I: ankle, hip, and spine. International Society of Biomechanics. , 2002, Journal of biomechanics.

[10]  S. Mitra,et al.  Gesture Recognition: A Survey , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Junke Li,et al.  Hand gesture recognition system using depth data , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[12]  Kalpana C. Jondhale,et al.  Hand Gesture Recognition System Using Standard Fuzzy C-Means Algorithm for Recognizing Hand Gesture with Angle Variations for Unsupervised Users , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[13]  Bohdan S. Butkiewicz,et al.  A Method for Automatic Membership Function Estimation Based on Fuzzy Measures , 2007, IFSA.

[14]  Bart Selman,et al.  Unstructured human activity detection from RGBD images , 2011, 2012 IEEE International Conference on Robotics and Automation.

[15]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[16]  Dave Wilkinson,et al.  A Hybrid GP-Fuzzy Approach for Resevoir Characterization , 2003 .

[17]  Honghai Liu,et al.  A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations , 2008, IEEE Transactions on Fuzzy Systems.

[18]  Junsong Yuan,et al.  Robust hand gesture recognition with kinect sensor , 2011, ACM Multimedia.

[19]  Wolfram Burgard,et al.  Graph-Based Action Models for Human Motion Classification , 2012, ROBOTIK.

[20]  Honghai Liu,et al.  Recognition of Human Motion From Qualitative Normalised Templates , 2007, J. Intell. Robotic Syst..

[21]  H. Bourlard,et al.  Links Between Markov Models and Multilayer Perceptrons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Mubarak Shah,et al.  Visual gesture recognition , 1994 .

[24]  R. Oka,et al.  Recognition of dexterous manipulations from time-varying images , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[25]  Wei Zeng,et al.  Depth and RGB image alignment for hand gesture segmentation using Kinect , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[26]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[27]  Lotfi A. Zadeh.  Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008 .

[28]  Adso Fernández-Baena,et al.  Biomechanical Validation of Upper-Body and Lower-Body Joint Movements of Kinect Motion Capture Data for Rehabilitation Treatments , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.

[29]  Tom Hintz,et al.  Neural network classifiers for automated video surveillance , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

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

[31]  Steve C. Maddock,et al.  Motion Capture File Formats Explained , 2001 .

[32]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[33]  Rachid Aissaoui,et al.  3D joint rotation measurement using MEMs inertial sensors: Application to the knee joint , 2006 .

[34]  Yinfei Dai,et al.  Study on Action Recognition Based on Kinect and Its Application in Rehabilitation Training , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[35]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[36]  G.R.S. Murthy,et al.  Hand gesture recognition using neural networks , 2010, 2010 IEEE 2nd International Advance Computing Conference (IACC).

[37]  Florica Moldoveanu,et al.  Human motion tracking & evaluation using Kinect V2 sensor , 2015, 2015 E-Health and Bioengineering Conference (EHB).

[38]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[39]  Junsong Yuan,et al.  Robust Part-Based Hand Gesture Recognition Using Kinect Sensor , 2013, IEEE Transactions on Multimedia.

[40]  Didier Dubois,et al.  Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification , 2001, Annals of Mathematics and Artificial Intelligence.

[41]  Aaron F. Bobick,et al.  A State-Based Approach to the Representation and Recognition of Gesture , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[43]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.