Human motion recognition using Gaussian Processes classification

This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize the motion properties. GP classification is then used to learn and predict motion categories. Experimental results on two real-world state-of-the-art datasets show that the proposed approach is effective, and outperforms support vector machine (SVM).

[1]  Ashok Veeraraghavan,et al.  The Function Space of an Activity , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Neil D. Lawrence,et al.  Extensions of the Informative Vector Machine , 2004, Deterministic and Statistical Methods in Machine Learning.

[3]  Svetha Venkatesh,et al.  Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Ehud Rivlin,et al.  Tracking and Classifying of Human Motions with Gaussian Process Annealed Particle Filter , 2007, ACCV.

[5]  Deng Cai,et al.  Tensor Subspace Analysis , 2005, NIPS.

[6]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[7]  Ronen Basri,et al.  Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Xiaozhe Wang,et al.  Characteristic-Based Clustering for Time Series Data , 2006, Data Mining and Knowledge Discovery.

[9]  Kotagiri Ramamohanarao,et al.  Characteristic-Based Descriptors for Motion Sequence Recognition , 2008, PAKDD.

[10]  Cristian Sminchisescu,et al.  Conditional Random Fields for Contextual Human Motion Recognition , 2005, ICCV.

[11]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .