# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 1 Introduction  
Crowd counting estimates the count of people and their spatial distribution in a given scenario. It is still an important research field of crowd analysis. With the growth in population and rapid development of the economy, more people pursue a better life that often flows in various sports, entertainment scenarios, well-known scenic areas, etc. However, due to improper control of crowd gathering, stampede accidents emerge one after another [1], which causes great losses of life and property. Therefore, a robust crowd counting algorithm can accurately estimate the number of people in a scene, to prevent effectively the accidents.  
With the high-speed improvement in computer technology, crowd counting has many applications in reality, such as public safety management, urban planning, and intelligent decision making. After a few decades of study, researchers have proposed many competitive crowd counting algorithms. Generally speaking, the method of machine learning was mainly used in early crowd counting. The characteristics of people were mapped and processed by detection [2] or regression [3], so as to obtain the sum of people in images [4; 5] or videos [6]. However, these processing methods cannot obtain high-level spatial distribution information from images. The count of large-scale objects was counted by a density estimation-based approach. It can not only estimate the density map but also account for the number of objects in any zone of a map by combining spatial information.  
In recent years, although researchers have devised some effective approaches, crowd counting is still a worthy task in dense scenarios. In recent years, these related researchershave their attention on CNN and have persistently studied CNN-based crowd counting in the same scene with different scales and different densities. The CNN-based approach involves two parts: (1) feature extraction: adaptive extraction of the feature is carried out by adding multiscale prediction, context awareness, empty convolution, and deformable convolution for different challenges; (2) density estimation map: a quality of density map is generated and modified by a loss function, such as structural similarity function [7], generating adversarial function and other methods.  
It is widely known that these traditional machine-learning-based methods can only extract low-level feature information, and most existing deep learning-based methods just employ a network to generate the estimation density map and count the number of people in dense scenarios, especially in the case of population heterogeneous distribution. Therefore, this research proposes a crowd counting method based on scene adaptive segmentation, which is called SASNet. First, we adopted adaptive horizontal segmentation for the image and obtained two regions with different density levels of close-up view and distant view. Second, we introduced two kinds of networks with different sizes of receptor fields to grab features of different scales in two regions, and enhance the robustness of the algorithm. Finally, a dilated convolution is employed to offset the increase in parameters due to the dual-column network.  
In this work, our contributions compared with previous work are summarized:  
1. A novel scene adaptive segmentation network is proposed that can adaptively divide the input image into distant-view regions and close-up view regions with different densities.
2. We exploit simultaneously distant view and close-up view crowd counting modules, which can extract features of different scales from the two views according to different receptive fields of image.
3. A dilated convolution is employed to lower greatly the number of parameters.