ScaleInformed Density Estimation for Dense Crowd Counting Dense crowd counting (DCC) remains challenging due to the scale variation and occlusion. Several deep learning based DCC methods have achieved the stateofarts on public datasets. However, experimental results show that the scale variation is still the main factor to hinder the DCC performance. In this work, we propose a scaleinformed dense crowd counting method focusing on handling the negative effect caused by scale variation. More specifically, we propose a method to obtain the scale information of the patch from its GT density maps via estimating the mean value of the Gaussian kernel width and then a scaleclassifier is deigned and trained accordingly. Moreover, with the estimated scale information, two subnets are dedicatedly deigned to learn the density maps for largescale head patch and smallscale patch separately. Experimental results validate the performance of our proposed method which achieves the best performance on three dense crowd datasets.

Deep Crowd Counting In Congested Scenes Through Refine Modules Crowd counting, which aims to predict the number of persons in a highly congested scene, has been widely explored and can be used in many applications like video surveillance, pedestrian flow, etc. The severe mutual occlusion among person, the large perspective distortion and the scale variations always hinder an accurate estimation. Although existing approaches have made much progress, there still has room for improvement. The drawbacks of existing methods are 2fold: (1)the scale information, which is an important factor for crowd counting, is always insufficiently explored and thus cannot bring wellestimated results; (2)using a unified framework for the whole image may result to a rough estimation in subregions, and thus leads to inaccurate estimation. Motivated by this, we propose a new method to address these problems. We first construct a crowdspecific and scaleaware convolutional neural network, which considers crowd scale variations and integrates multiscale feature representations in the Cross Scale Module (CSM), to produce the initial predicted density map. Then the proposed Local Refine Modules (LRMs) are performed to gradually reestimate predictions of subregions. We conduct experiments on three crowd counting datasets (the ShanghaiTech dataset, the UCF_CC_50 dataset and the UCSD dataset). Experiments show that our proposed method achieves superior performance compared with the stateofthearts. Besides, we conduct experiments on counting vehicles in the TRANCOS dataset and get better results, which proves the generalization ability of the proposed method.