Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time tracker with fast recovery from target loss   In this paper, we introduce a variation of a state-of-the-art real-time
tracker (CFNet), which adds to the original algorithm robustness to target loss
without a significant computational overhead. The new method is based on the
assumption that the feature map can be used to estimate the tracking confidence
more accurately. When the confidence is low, we avoid updating the object's
position through the feature map; instead, the tracker passes to a single-frame
failure mode, during which the patch's low-level visual content is used to
swiftly update the object's position, before recovering from the target loss in
the next frame. The experimental evidence provided by evaluating the method on
several tracking datasets validates both the theoretical assumption that the
feature map is associated to tracking confidence, and that the proposed
implementation can achieve target recovery in multiple scenarios, without
compromising the real-time performance.

36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin