Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hierarchical Deep Temporal Model for Group Activity Recognition   In group activity recognition, the temporal dynamics of the whole activity
can be inferred based on the dynamics of the individual people representing the
activity. We build a deep model to capture these dynamics based on LSTM
(long-short term memory) models. To make use of these ob- servations, we
present a 2-stage deep temporal model for the group activity recognition
problem. In our model, a LSTM model is designed to represent action dynamics of
in- dividual people in a sequence and another LSTM model is designed to
aggregate human-level information for whole activity understanding. We evaluate
our model over two datasets: the collective activity dataset and a new volley-
ball dataset. Experimental results demonstrate that our proposed model improves
group activity recognition perfor- mance with compared to baseline methods.

17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph