Spatiotemporal projection of motion field sequence for generating feature vectors in gesture perception

An algorithm of generating a single feature vector representation from a sequence of motion fields has been developed for gesture perception. Four directional motion components in a motion field are projected onto x- and y-axes perpendicular to respective directions and four histograms are generated. Then the time evolution of such histograms are integrated by spatiotemporal projection and summarized in the form of a single vector that represents the motion history of the image in a scene. Gesture perception is carried out by a simple template matching using such motion-field sequence vector. In order to enhance the performance of gesture perception, three techniques have been introduced in template matching: normalization, smoothing and shifting. Preliminary gesture perception experiments using the vector representation developed in the present work have been carried out and the effectiveness of the algorithm has been demonstrated.

[1]  James W. Davis,et al.  Real-time recognition of activity using temporal templates , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[2]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  S. Mitra,et al.  Gesture Recognition: A Survey , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[4]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[5]  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.

[6]  Tadashi Shibata,et al.  Block-matching-based motion field generation utilizing directional edge displacement , 2010, Comput. Electr. Eng..

[7]  Ian D. Reid,et al.  Joint Bayes Filter: A Hybrid Tracker for Non-rigid Hand Motion Recognition , 2004, ECCV.

[8]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[9]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[10]  Thad Starner,et al.  Visual Recognition of American Sign Language Using Hidden Markov Models. , 1995 .

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

[12]  Randal C. Nelson,et al.  Recognition of motion from temporal texture , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[14]  Philip Kahn,et al.  Integrating moving edge information along a 2D trajectory in densely sampled imagery , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Michael J. Black,et al.  Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[16]  J. H. Duncan,et al.  On the Detection of Motion and the Computation of Optical Flow , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Tieniu Tan,et al.  Gesture recognition using temporal template based trajectories , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..