Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Sports Camera Selection from Internet Videos   This work addresses camera selection, the task of predicting which camera
should be "on air" from multiple candidate cameras for soccer broadcast. The
task is challenging because of the scarcity of learning data with all candidate
views. Meanwhile, broadcast videos are freely available on the Internet (e.g.
Youtube). However, these videos only record the selected camera views, omitting
the other candidate views. To overcome this problem, we first introduce a
random survival forest (RSF) method to impute the incomplete data effectively.
Then, we propose a spatial-appearance heatmap to describe foreground objects
(e.g. players and balls) in an image. To evaluate the performance of our
system, we collect the largest-ever dataset for soccer broadcasting camera
selection. It has one main game which has all candidate views and twelve
auxiliary games which only have the broadcast view. Our method significantly
outperforms state-of-the-art methods on this challenging dataset. Further
analysis suggests that the improvement in performance is indeed from the extra
information from auxiliary games.

18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con