Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

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The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg