54,046 research outputs found
Efficient online subspace learning with an indefinite kernel for visual tracking and recognition
We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an incremental KPCA in Krein space that does not require the calculation of preimages and therefore is both efficient and exact. Our approach has been motivated by the application of visual tracking for which we wish to employ a robust gradient-based kernel. We use the proposed nonlinear appearance model learned online via KPCA in Krein space for visual tracking in many popular and difficult tracking scenarios. We also show applications of our kernel framework for the problem of face recognition
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
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Most sparse linear representation-based trackers need to solve a
computationally expensive L1-regularized optimization problem. To address this
problem, we propose a visual tracker based on non-sparse linear
representations, which admit an efficient closed-form solution without
sacrificing accuracy. Moreover, in order to capture the correlation information
between different feature dimensions, we learn a Mahalanobis distance metric in
an online fashion and incorporate the learned metric into the optimization
problem for obtaining the linear representation. We show that online metric
learning using proximity comparison significantly improves the robustness of
the tracking, especially on those sequences exhibiting drastic appearance
changes. Furthermore, in order to prevent the unbounded growth in the number of
training samples for the metric learning, we design a time-weighted reservoir
sampling method to maintain and update limited-sized foreground and background
sample buffers for balancing sample diversity and adaptability. Experimental
results on challenging videos demonstrate the effectiveness and robustness of
the proposed tracker.Comment: Appearing in IEEE Conf. Computer Vision and Pattern Recognition, 201
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
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