Template-based human motion recognition for complex activities

We have presented motion history-based human motion recognition technique with various formats of feature vectors. Since the inception of the motion history image (MHI) template for motion recognition, various progresses have been adopted to improve this basic MHI. Stages of development of appearance-based representation and recognition approach are presented here on the basic motion history-based approach to solve self-occlusion problem using our method. Excellent recognition rate for various motions has been found. This is based on gradient-based optical flow calculation. For recognition, Hu moments are considered to calculate feature vectors. Various feature vectors are considered in this paper.

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

[2]  Robert T. Collins,et al.  Moving Object Localization in Thermal Imagery by Forward-backward MHI , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[3]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Trevor Beugeling,et al.  Analysis of Irregularities in Human Actions with Volumetric Motion History Images , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[6]  S. Kumar,et al.  Classification of hand movements using motion templates and geometrical based moments , 2004, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of.

[7]  Do Lenh Hung Son,et al.  Detection and localization of road area in traffic video sequences using motion information and fuzzy-shadowed sets , 2005, Seventh IEEE International Symposium on Multimedia (ISM'05).

[8]  S. Ishikawa,et al.  Human activity recognition: Various paradigms , 2008, 2008 International Conference on Control, Automation and Systems.

[9]  Sang-Woong Lee,et al.  Real-Time Gesture Recognition Using 3D Motion History Model , 2005, ICIC.

[10]  S. Ishikawa,et al.  Performance of Multi-directional MHI for Human Motion Recognition in the Presence of Outliers , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[11]  István Petrás,et al.  Flexible test-bed for unusual behavior detection , 2007, CIVR '07.

[12]  Shaogang Gong,et al.  Learning pixel-wise signal energy for understanding semantics , 2003, Image Vis. Comput..

[13]  Thomas H. Reiss,et al.  The revised Fundamental Theorem of Moment Invariants , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Hon-Son Don,et al.  Pattern recognition using 3-D moments , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

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

[16]  Nanning Zheng,et al.  Gait History Image: A Novel Temporal Template for Gait Recognition , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[17]  Jr. Joseph J. LaViola,et al.  A Survey of Hand Posture and Gesture Recognition Techniques and Technology , 1999 .

[18]  Dinggang Shen,et al.  Discriminative wavelet shape descriptors for recognition of 2-D patterns , 1999, Pattern Recognit..

[19]  Alex Pentland,et al.  Understanding purposeful human motion , 1999, Proceedings IEEE International Workshop on Modelling People. MPeople'99.

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

[21]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[22]  Sanjay Kumar,et al.  Visual Speech Recognition Using Image Moments and Multiresolution Wavelet Images , 2006, International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06).

[23]  Gary Bradski,et al.  Real-time Motion Template Gradients using Intel CVLib , 1999 .

[24]  Sang-Woong Lee,et al.  Volume Motion Template for View-Invariant Gesture Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[25]  Sameer Singh,et al.  Video analysis of human dynamics - a survey , 2003, Real Time Imaging.

[26]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

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

[28]  Montse Pardàs,et al.  Human model and motion based 3D action recognition in multiple view scenarios , 2006, 2006 14th European Signal Processing Conference.

[29]  Maja Pantic,et al.  Motion history for facial action detection in video , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[30]  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).

[31]  Li Li,et al.  Spatio-temporal motion segmentation and tracking under realistic condition , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[32]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[33]  T. Jan,et al.  Neural network based threat assessment for automated visual surveillance , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[34]  Xiaoli Yang,et al.  A comparative study of Fourier descriptors and Hu's seven moment invariants for image recognition , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[35]  Joo Kooi Tan,et al.  High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace , 2006, IEICE Trans. Inf. Syst..

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

[37]  Gary R. Bradski,et al.  Motion segmentation and pose recognition with motion history gradients , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.