Directional Stationary Wavelet-Based Representation for Human Action Classification

This paper proposes a directional wavelet-based representation of natural human actions in realistic videos. This task is very important for human action recognition, which has become one of the most important fields in computer vision. Its importance comes from the large number of applications that employ human action classification and recognition. The proposed method utilizes the 3D Stationary Wavelet Analysis to encode the directional spatio-temporal characteristics of the motion available in video sequences. It was tested using the Weizmann dataset, and produced promising preliminary results (92.47 % classification accuracy) when compared to existing state–of–the–art methods.

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

[2]  Alessio Del Bue,et al.  Human behavior analysis in video surveillance: A Social Signal Processing perspective , 2013, Neurocomputing.

[3]  Mohamed F. Tolba,et al.  Spatio-Temporal Motion Detection for Intelligent Surveillance Applications , 2015 .

[4]  Luc Van Gool,et al.  Action snippets: How many frames does human action recognition require? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Alex Pentland,et al.  Human-Centred Intelligent Human-Computer Interaction (HCI2): how far are we from attaining it? , 2008, Int. J. Auton. Adapt. Commun. Syst..

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

[7]  Li Wang,et al.  Structured learning of local features for human action classification and localization , 2012, Image Vis. Comput..

[8]  Jin Young Choi,et al.  Intelligent visual surveillance — A survey , 2010 .

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

[10]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[11]  Arun Sharma,et al.  Wavelet directional histograms for classification of human gestures represented by spatio-temporal templates , 2004, 10th International Multimedia Modelling Conference, 2004. Proceedings..

[12]  James W. Davis,et al.  The representation and recognition of human movement using temporal templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Yannis Avrithis,et al.  Spatiotemporal saliency for event detection and representation in the 3D wavelet domain: potential in human action recognition , 2007, CIVR '07.

[14]  Alberto Del Bimbo,et al.  Recognizing human actions by fusing spatio-temporal appearance and motion descriptors , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[15]  Md. Atiqur Rahman Ahad,et al.  Motion history image: its variants and applications , 2012, Machine Vision and Applications.

[16]  Alex Pentland,et al.  Human computing and machine understanding of human behavior: a survey , 2006, ICMI '06.

[17]  Yannis Avrithis,et al.  Spatiotemporal saliency for video classification , 2009, Signal Process. Image Commun..

[18]  Anupam Agrawal,et al.  A survey on activity recognition and behavior understanding in video surveillance , 2012, The Visual Computer.

[19]  Sharon Oviatt,et al.  Multimodal Interfaces , 2008, Encyclopedia of Multimedia.

[20]  R. Venkatesh Babu,et al.  Recognition of human actions using motion history information extracted from the compressed video , 2004, Image Vis. Comput..

[21]  Alex Pentland,et al.  Machine Understanding of Human Behavior , 2007 .

[22]  Xiaoqin Zhang,et al.  Adaptive learning codebook for action recognition , 2011, Pattern Recognit. Lett..

[23]  Shaogang Gong,et al.  Fusing appearance and distribution information of interest points for action recognition , 2012, Pattern Recognit..

[24]  Ling Shao,et al.  Human action segmentation and recognition via motion and shape analysis , 2012, Pattern Recognit. Lett..

[25]  Yupin Luo,et al.  Recognizing human actions using a new descriptor based on spatial-temporal interest points and weighted-output classifier , 2012, Neurocomputing.

[26]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

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

[28]  Mubarak Shah,et al.  A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.

[29]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[30]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[31]  Rémi Ronfard,et al.  A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..