Motion feature detection using steerable flow fields

(with David Fleet and Allan Jepson)

The estimation and detection of occlusion boundaries and moving bars are important and challenging problems in image sequence analysis. Here, we model such motion features as linear combinations of steerable basis flow fields. These models constrain the interpretation of image motion, and are used in the same way as translational or affine motion models. We estimate the subspace coefficients of our motion feature models directly from spatiotemporal image derivatives using a robust regression method. From the subspace coefficients we detect the presence of a motion feature and solve for the orientation of the feature and the relative velocities of the surfaces. Our method does not require the prior computation of optical flow and recovers accurate estimates of orientation and velocity.

Related Publications

Fleet, D. J., Black, M. J., and Jepson, A. D., Motion feature detection using steerable flow fields to appear: IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-98, Santa Barbara, CA, 1998.