Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Over-crowdedness Alert! Forecasting the Future Crowd Distribution   In recent years, vision-based crowd analysis has been studied extensively due
to its practical applications in real world. In this paper, we formulate a
novel crowd analysis problem, in which we aim to predict the crowd distribution
in the near future given sequential frames of a crowd video without any
identity annotations. Studying this research problem will benefit applications
concerned with forecasting crowd dynamics. To solve this problem, we propose a
global-residual two-stream recurrent network, which leverages the consecutive
crowd video frames as inputs and their corresponding density maps as auxiliary
information to predict the future crowd distribution. Moreover, to strengthen
the capability of our network, we synthesize scene-specific crowd density maps
using simulated data for pretraining. Finally, we demonstrate that our
framework is able to predict the crowd distribution for different crowd
scenarios and we delve into applications including predicting future crowd
count, forecasting high-density region, etc.

17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int