Learning sparse representations for view-independent human action recognition based on fuzzy distances

In this paper, a method aiming at view-independent human action recognition is presented. Actions are described as series of successive human body poses. Action videos representation is based on fuzzy vector quantization, while action classification is performed by a novel classification algorithm, the so-called Sparsity-based Learning Machine (SbLM), involving two optimization steps. The first one determines a non-linear data mapping to a high-dimensional feature space determined by an l1-minimization process exploiting an overcomplete dictionary formed by the training samples. The second one, involves a training process in order to determine the optimal separating hyperplanes in the resulted high-dimensional feature space. The performance of the proposed human action recognition method is evaluated on two publicly available action recognition databases aiming at different application scenarios.

[1]  Mohan M. Trivedi,et al.  Human body modelling and tracking using volumetric representation: Selected recent studies and possibilities for extensions , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[2]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[3]  T. Poggio,et al.  Cognitive neuroscience: Neural mechanisms for the recognition of biological movements , 2003, Nature Reviews Neuroscience.

[4]  Tieniu Tan,et al.  Robust view transformation model for gait recognition , 2011, 2011 18th IEEE International Conference on Image Processing.

[5]  E. Candes,et al.  11-magic : Recovery of sparse signals via convex programming , 2005 .

[6]  Rémi Ronfard,et al.  Automatic Discovery of Action Taxonomies from Multiple Views , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Ioannis Pitas,et al.  View indepedent human movement recognition from multi-view video exploiting a circular invariant posture representation , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[8]  Qinghua Zheng,et al.  Regularized Extreme Learning Machine , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[9]  Anastasios Tefas,et al.  Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Alexandros Iosifidis,et al.  Eating and drinking activity recognition based on discriminant analysis of fuzzy distances and activity volumes , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Alexandros Iosifidis,et al.  Activity-Based Person Identification Using Fuzzy Representation and Discriminant Learning , 2012, IEEE Transactions on Information Forensics and Security.

[12]  Yang Yang,et al.  Human action recognition using sparse representation , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[13]  Dong Han,et al.  Spatiotemporal Sparsity Induced Similarity Measure for Human Action Recognition , 2010, J. Digit. Content Technol. its Appl..

[14]  Qiang Wu,et al.  Multi-view Gait Recognition Based on Motion Regression Using Multilayer Perceptron , 2010, 2010 20th International Conference on Pattern Recognition.

[15]  Q. M. Jonathan Wu,et al.  Human action recognition using extreme learning machine based on visual vocabularies , 2010, Neurocomputing.

[16]  Rama Chellappa,et al.  Sparse dictionary-based representation and recognition of action attributes , 2011, 2011 International Conference on Computer Vision.

[17]  Mubarak Shah,et al.  Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Janusz Konrad,et al.  Action Recognition Using Sparse Representation on Covariance Manifolds of Optical Flow , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[19]  Tanaya Guha,et al.  Learning Sparse Representations for Human Action Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Demetri Terzopoulos,et al.  Surveillance camera scheduling: a virtual vision approach , 2005, Multimedia Systems.

[21]  Rémi Ronfard,et al.  Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..

[22]  Tieniu Tan,et al.  Modelling the Effect of View Angle Variation on Appearance-Based Gait Recognition , 2006, ACCV.

[23]  Alexandros Iosifidis,et al.  Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis , 2012, Comput. Vis. Image Underst..

[24]  Alexandros Iosifidis,et al.  Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis , 2013, Signal Process..

[25]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Ling Shao,et al.  Transform based spatio-temporal descriptors for human action recognition , 2011, Neurocomputing.

[28]  Ioannis Pitas,et al.  The i3DPost Multi-View and 3D Human Action/Interaction Database , 2009, 2009 Conference for Visual Media Production.

[29]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[30]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[31]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[32]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[33]  Allen Y. Yang,et al.  Fast ℓ1-minimization algorithms and an application in robust face recognition: A review , 2010, 2010 IEEE International Conference on Image Processing.

[34]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[35]  Müjdat Çetin,et al.  A group sparsity-driven approach to 3-D action recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[36]  Juan José Pantrigo,et al.  Representation spaces in a visual-based human action recognition system , 2009, Neurocomputing.

[37]  Yongtian Wang,et al.  Sparse representation for action recognition , 2010, 2010 3rd International Congress on Image and Signal Processing.

[38]  Alexandros Iosifidis,et al.  View-Invariant Action Recognition Based on Artificial Neural Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[39]  David G. Stork,et al.  Pattern Classification , 1973 .