Cardboard people: a parameterized model of articulated image motion

We extend the work of Black and Yacoob (1995) on the tracking and recognition of human facial expressions using parametrized models of optical flow to deal with the articulated motion of human limbs. We define a "card-board person model" in which a person's limbs are represented by a set of connected planar patches. The parametrized image motion of these patches in constrained to enforce articulated motion and is solved for directly using a robust estimation technique. The recovered motion parameters provide a rich and concise description of the activity that can be used for recognition. We propose a method for performing view-based recognition of human activities from the optical flow parameters that extends previous methods to cope with the cyclical nature of human motion. We illustrate the method with examples of tracking human legs of long image sequences.

[1]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[2]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[3]  Alex Pentland,et al.  Recovery of non-rigid motion and structure , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Alex Pentland,et al.  Recovery of Nonrigid Motion and Structure , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Michael Isard,et al.  Learning to track curves in motion , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[6]  J. Aggarwal,et al.  Lower limb kinematics of human walking with the medial axis transformation , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

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

[8]  David C. Hogg,et al.  An efficient method for contour tracking using active shape models , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[9]  Ioannis A. Kakadiaris,et al.  Active part-decomposition, shape and motion estimation of articulated objects: a physics-based approach , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Steven M. Seitz,et al.  Affine invariant detection of periodic motion , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  K. Rohr.  Towards model-based recognition of human movements in image sequences , 1994 .

[12]  Charles Kervrann,et al.  A hierarchical statistical framework for the segmentation of deformable objects in image sequences , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  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.

[14]  Pietro Perona,et al.  Monocular tracking of the human arm in 3D , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  Michael J. Black,et al.  Skin and bones: multi-layer, locally affine, optical flow and regularization with transparency , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Larry S. Davis,et al.  3-D model-based tracking of humans in action: a multi-view approach , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..