8,642 research outputs found
Multi-Scale 3D Scene Flow from Binocular Stereo Sequences
Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108
Bags of Affine Subspaces for Robust Object Tracking
We propose an adaptive tracking algorithm where the object is modelled as a
continuously updated bag of affine subspaces, with each subspace constructed
from the object's appearance over several consecutive frames. In contrast to
linear subspaces, affine subspaces explicitly model the origin of subspaces.
Furthermore, instead of using a brittle point-to-subspace distance during the
search for the object in a new frame, we propose to use a subspace-to-subspace
distance by representing candidate image areas also as affine subspaces.
Distances between subspaces are then obtained by exploiting the non-Euclidean
geometry of Grassmann manifolds. Experiments on challenging videos (containing
object occlusions, deformations, as well as variations in pose and
illumination) indicate that the proposed method achieves higher tracking
accuracy than several recent discriminative trackers.Comment: in International Conference on Digital Image Computing: Techniques
and Applications, 201
Generalized Kernel-based Visual Tracking
In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page
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