35,794 research outputs found
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
In this paper we propose a technique to adapt a convolutional neural network
(CNN) based object counter to additional visual domains and object types while
still preserving the original counting function. Domain-specific normalisation
and scaling operators are trained to allow the model to adjust to the
statistical distributions of the various visual domains. The developed
adaptation technique is used to produce a singular patch-based counting
regressor capable of counting various object types including people, vehicles,
cell nuclei and wildlife. As part of this study a challenging new cell counting
dataset in the context of tissue culture and patient diagnosis is constructed.
This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is
the first of its kind to be made available to the wider computer vision
community. State-of-the-art object counting performance is achieved in both the
Shanghaitech (parts A and B) and Penguins datasets while competitive
performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets,
all using a shared counting model.Comment: 10 page
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
- …