@article{ref1,
title="Fine-grained crowd counting",
journal="IEEE transactions on image processing",
year="2021",
author="Kumar, Nikil Senthil and Wan, Jia and Chan, Antoni B.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Current crowd counting algorithms are only concerned about the number of people in an image, which lacks low-level fine-grained information of the crowd. For many  practical applications, the total number of people in an image is not as useful as  the number of people in each sub-category. For example, knowing the number of people  waiting inline or browsing can help retail stores; knowing the number of people  standing/sitting can help restaurants/cafeterias; knowing the number of  violent/non-violent people can help police in crowd management. In this paper, we  propose fine-grained crowd counting, which differentiates a crowd into categories  based on the low-level behavior attributes of the individuals (e.g. standing/sitting  or violent behavior) and then counts the number of people in each category. To  enable research in this area, we construct a new dataset of four real-world  fine-grained counting tasks: traveling direction on a sidewalk, standing or sitting,  waiting in line or not, and exhibiting violent behavior or not. Since the appearance  features of different crowd categories are similar, the challenge of fine-grained  crowd counting is to effectively utilize contextual information to distinguish  between categories. We propose a two branch architecture, consisting of a density  map estimation branch and a semantic segmentation branch. We propose two refinement  strategies for improving the predictions of the two branches. First, to encode  contextual information, we propose feature propagation guided by the density map  prediction, which eliminates the effect of background features during propagation. Second, we propose a complementary attention model to share information between the  two branches. Experiment results confirm the effectiveness of our method.<p /> <p>Language: en</p>",
language="en",
issn="1057-7149",
doi="10.1109/TIP.2021.3049938",
url="http://dx.doi.org/10.1109/TIP.2021.3049938"
}