Fast Simplex-HMM for One-Shot Learning Activity Recognition

The work presented in this paper deals with the challenging task of learning an activity class representation using a single sequence for training. Recently, Simplex-HMM framework has been shown to be an efficient representation for activity classes, however, it presents high computational costs making it impractical in several situations. A dimensionality reduction of the features spaces based on a Maximum at Posteriori adaptation combined with a fast estimation of the optimal parameters in the Expectation Maximization algorithm are presented in this paper. As confirmed by the experimental results, these two modifications not only reduce the computational cost but also maintain the performance or even improve it. The process suitability is experimentally confirmed using the human activity datasets Weizmann, KTH and IXMAS and the gesture dataset ChaLearn.

[1]  Xi Wang,et al.  Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification , 2015, ACM Multimedia.

[2]  Peyman Milanfar,et al.  Action Recognition from One Example , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Mubarak Shah,et al.  UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.

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

[5]  Mubarak Shah,et al.  Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[7]  Tal Hassner,et al.  Motion Interchange Patterns for Action Recognition in Unconstrained Videos , 2012, ECCV.

[8]  Carlos Orrite-Uruñuela,et al.  One-Sequence Learning of Human Actions , 2011, HBU.

[9]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  Cordelia Schmid,et al.  Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Juan Carlos Niebles,et al.  Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification , 2010, ECCV.

[13]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Larry S. Davis,et al.  AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video , 2011, AVSS.

[15]  Mubarak Shah,et al.  Actions sketch: a novel action representation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  James W. Davis,et al.  Real-time recognition of activity using temporal templates , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[17]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[18]  T. Minka.  Estimating a Dirichlet distribution , 2012 .

[19]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[20]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.

[21]  Thomas Serre,et al.  HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.

[22]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[23]  Isabelle Guyon,et al.  The ChaLearn gesture dataset (CGD 2011) , 2014, Machine Vision and Applications.

[24]  Carlos Medrano,et al.  Transfer Learning of Human Poses for Action Recognition , 2013, HBU.

[25]  Dimitrios Makris,et al.  One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM , 2017, IEEE Transactions on Cybernetics.

[26]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

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