Human activity recognition based on silhouette analysis using Local Binary Patterns

In this paper, we present a novel method for activity analysis in outdoor environment. Difficulties, such as low resolution, shadows, long distances, and segmentation problems, usually exist in outdoor environment. To deal with these difficulties, the activity width sequence image which maintains 3D features using 2D representation is exploited to represent the silhouette structure information of each frame and dynamic properties of activity. The activity width vectors are converted to the gray value successively according to the order in activity sequence and the gray image is formed in spatio-temporal space. We regard the activity width sequence image as the texture and choose Local Binary Patterns which is a powerful texture extraction operator to analyze the spatio-temporal pattern. The method is simple and effective. We test our method based on the outdoor database and the results are encouraging.

[1]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[2]  Irfan A. Essa,et al.  Learning Temporal Sequence Model from Partially Labeled Data , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Irfan A. Essa,et al.  Recognizing multitasked activities from video using stochastic context-free grammar , 2002, AAAI/IAAI.

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

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

[6]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  B. Prabhakaran,et al.  SAMHIS: A Robust Motion Space for Human Activity Recognition , 2012, 2012 IEEE International Symposium on Multimedia.

[9]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Han Su,et al.  A new method of gait recognition independent of view angle , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[11]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Jake K. Aggarwal,et al.  Recognition of Composite Human Activities through Context-Free Grammar Based Representation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[14]  G. Johansson.  Visual perception of biological motion and a model for its analysis , 1973 .

[15]  Mubarak Shah,et al.  View-Invariant Representation and Recognition of Actions , 2002, International Journal of Computer Vision.

[16]  Han Su,et al.  A gait recognition method using L1-PCA and LDA , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[17]  Alexander J. Smola,et al.  Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes , 2007, International Journal of Computer Vision.