Direction-oriented human motion recognition with prior estimation of directions

As the facilities are becoming available with the advent of the state-of-art technologies, the necessity of man-machine interaction systems is growing day-by-day. Within various applications of such a system, one of the most promising applications in the field of computer vision is the understanding and the interpretation of human motion or behavior in a scene. Direction-oriented motion capture of a person performing some tasks is an important issue in developing a human motion recognition system, since an intelligent system should also be incorporated with the directional information. We propose a direction-oriented motion recognition approach that makes use of the directional information by prior estimation. This reduces the processing time of the system by excluding unnecessary searching for the most similar motions. In this approach, direction-wise motions are clustered within the feature space in order to make the direction estimation easier. Each motion is converted to individual template, namely Motion History Image (MHI) and Exclusive-OR (XOR) Image, by extracting distinguishable features from the video-clips containing the motions. A Structured Motion Database (SMoDB) is developed to match an unlabeled motion against the pre-stored motions. Experiments are conducted on an Avatar dataset and significant improvements in the results are noticed.

[1]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[2]  Joo Kooi Tan,et al.  An efficient approach to human motion recognition employing large motion-database structure , 2008, 2008 SICE Annual Conference.

[3]  Donald J. Berndt,et al.  Finding Patterns in Time Series: A Dynamic Programming Approach , 1996, Advances in Knowledge Discovery and Data Mining.

[4]  Hiroshi Murase,et al.  Moving object recognition in eigenspace representation: gait analysis and lip reading , 1996, Pattern Recognit. Lett..

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

[6]  Osama Masoud,et al.  A method for human action recognition , 2003, Image Vis. Comput..

[7]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

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

[9]  Eraldo Ribeiro,et al.  Human Motion Recognition Using Isomap and Dynamic Time Warping , 2007, Workshop on Human Motion.

[10]  Liang Wang,et al.  Analyzing Human Movements from Silhouettes Using Manifold Learning , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[11]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[13]  Rudolf Bayer,et al.  Organization and maintenance of large ordered indexes , 1972, Acta Informatica.

[14]  A. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, CVPR 2004.

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

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

[17]  Hiroyuki Fujioka,et al.  Motion recovery under perspective stereo vision , 2008 .

[18]  Joo Kooi Tan,et al.  Recognizing human motions from surrounding viewpoints employing hierarchical eigenspaces , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[19]  Michael J. Freeman,et al.  Motion history histograms for human action recognition , 2009 .

[20]  Joo Kooi Tan,et al.  High Accuracy and Real Time Recognition of Human Activities , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[21]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[22]  T. K. Vintsyuk.  Speech discrimination by dynamic programming , 1968 .

[23]  Joo Kooi Tan,et al.  Improvement of a Structured Motion Database for High Accuracy Human Motion Recognition , 2012, SOCO 2012.