Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Tracking Parameter Adaptation based on Evaluation   Parameter tuning is a common issue for many tracking algorithms. In order to
solve this problem, this paper proposes an online parameter tuning to adapt a
tracking algorithm to various scene contexts. In an offline training phase,
this approach learns how to tune the tracker parameters to cope with different
contexts. In the online control phase, once the tracking quality is evaluated
as not good enough, the proposed approach computes the current context and
tunes the tracking parameters using the learned values. The experimental
results show that the proposed approach improves the performance of the
tracking algorithm and outperforms recent state of the art trackers. This paper
brings two contributions: (1) an online tracking evaluation, and (2) a method
to adapt online tracking parameters to scene contexts.

24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg