Human Density Estimation by Exploiting Deep Spatial Contextual Information We propose in this paper a deep learning method for human density estimation, targeted to sparse density scenarios. We divide the image into a grid of cells and realize that a person does not span in one cell but several cells. To estimate the human density appeared in one cell, the information of surrounding cells plays a crucial role, socalled spatial contextual information. The key insight of our approach is the employment of Convolutional Neural Networks (CNN) to extract image features and Long ShortTerm Memory (LSTM) to exploit the spatial contextual information. The sharedweight mechanism of CNN models generalizes the learned features of humanity in all corners of the images. The feedback mechanism of LSTM captures spatial contextual information of neighboring cells in a subgrid of 3x3 cells to estimate the human density of the central cell. We demonstrate the validity of the proposed method on Mall dataset with a competitive performance in comparison with stateoftheart methods, validated on the same dataset.

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.