6,656 research outputs found
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
In real-world crowd counting applications, the crowd densities vary greatly
in spatial and temporal domains. A detection based counting method will
estimate crowds accurately in low density scenes, while its reliability in
congested areas is downgraded. A regression based approach, on the other hand,
captures the general density information in crowded regions. Without knowing
the location of each person, it tends to overestimate the count in low density
areas. Thus, exclusively using either one of them is not sufficient to handle
all kinds of scenes with varying densities. To address this issue, a novel
end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density
Estimation Network) is proposed. It can adaptively decide the appropriate
counting mode for different locations on the image based on its real density
conditions. DecideNet starts with estimating the crowd density by generating
detection and regression based density maps separately. To capture inevitable
variation in densities, it incorporates an attention module, meant to
adaptively assess the reliability of the two types of estimations. The final
crowd counts are obtained with the guidance of the attention module to adopt
suitable estimations from the two kinds of density maps. Experimental results
show that our method achieves state-of-the-art performance on three challenging
crowd counting datasets.Comment: CVPR 201
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
Crowd counting, i.e., estimating the number of people in a crowded area, has
attracted much interest in the research community. Although many attempts have
been reported, crowd counting remains an open real-world problem due to the
vast scale variations in crowd density within the interested area, and severe
occlusion among the crowd. In this paper, we propose a novel Pyramid
Density-Aware Attention-based network, abbreviated as PDANet, that leverages
the attention, pyramid scale feature and two branch decoder modules for
density-aware crowd counting. The PDANet utilizes these modules to extract
different scale features, focus on the relevant information, and suppress the
misleading ones. We also address the variation of crowdedness levels among
different images with an exclusive Density-Aware Decoder (DAD). For this
purpose, a classifier evaluates the density level of the input features and
then passes them to the corresponding high and low crowded DAD modules.
Finally, we generate an overall density map by considering the summation of low
and high crowded density maps as spatial attention. Meanwhile, we employ two
losses to create a precise density map for the input scene. Extensive
evaluations conducted on the challenging benchmark datasets well demonstrate
the superior performance of the proposed PDANet in terms of the accuracy of
counting and generated density maps over the well-known state of the arts
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