153,137 research outputs found
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
Online Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and
Machine Intelligenc
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