  In crowd behavior understanding, a model of crowd behavior need to be trained
using the information extracted from video sequences. Since there is no
ground-truth available in crowd datasets except the crowd behavior labels, most
of the methods proposed so far are just based on low-level visual features.
However, there is a huge semantic gap between low-level motion/appearance
features and high-level concept of crowd behaviors. In this paper we propose an
attribute-based strategy to alleviate this problem. While similar strategies
have been recently adopted for object and action recognition, as far as we
know, we are the first showing that the crowd emotions can be used as
attributes for crowd behavior understanding. The main idea is to train a set of
emotion-based classifiers, which can subsequently be used to represent the
crowd motion. For this purpose, we collect a big dataset of video clips and
provide them with both annotations of "crowd behaviors" and "crowd emotions".
We show the results of the proposed method on our dataset, which demonstrate
that the crowd emotions enable the construction of more descriptive models for
crowd behaviors. We aim at publishing the dataset with the article, to be used
as a benchmark for the communities.
