… -guided convolution (PGC) for convolutional neural network (CNN) based crowd counting
(ie … also introduce Crowd Surveillance, a large scale dataset for crowd counting that contains …

… Our approach for crowd counting relies instead on deep learnt features using the framework
… for crowd analysis. Our network estimates both the crowd count as well as the crowd density …

… of most existing crowd counting … convolutional neural network (CNN) for crowd counting,
and it is trained alternatively with two related learning objectives, crowd density and crowd count…

… convolution neural network (CNN)-based crowd counting and density estimation. We explored
the existing approaches from different perspectives, including the network … crowd counting …

… is used to extract crowd features. Then two supervisory signals, ie, crowd count and crowd
density, are employed to learn crowd features and estimate the specific counting. We test our …

… We evaluate our multi-scale convolutional neural network (MSCNN) for crowd counting on
two different datasets… All of the convolutional neural networks are trained based on Caffe [19]. …

… In order to tackle them, we propose a perspective crowd counting network (PCC Net),
which consists of three parts: 1) density map estimation (DME) focuses on learning very local …

… -the-art for crowd counting in high density … Convolution 6”. A 2D integration (simply an
element-wise sum in this case) is applied to the network output in order to produce the crowd count …

… crowd counting. The existing datasets are taken from outdoors and do not adequately cover
those crowd … the state-of-the-art crowd counting methods. We also compare SaCNN with …

… network, while a deeper network has been proved to have better performance in crowd counting
… the feature maps generated by MultiColumn Convolutional Neural Network (MCNN) [40]…