# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 1 Introduction  
Crowd safety in public places has always been a significant but troublesome affair, especially in high-density crowd gathering places. The higher the crowd density is, the easier of out of control will be [1], and it can cause serious casualties. It is important to seek an intelligent method of crowd analysis in public places to assist in prevention and decision making. As an important part of crowd analysis [2], crowd counting and density estimation can help to quantify the importance of events and provide relevant personnel with information to support decision-making. Therefore, crowd counting and its density estimation become hot topics in the security field, which are widely used in video surveillance, traffic monitoring, public safety and urban planning [3]. Nowadays, there is a very large demand for a crowd monitoring system. However, the existing crowd monitoring system products still have many weakness, for example, most of them are limited by application scenes or have low precision. Especially, research on monitoring the number of pedestrians in a large-scale crowded environment (Refer to Figure 1) is insufficient [4]. The crowd counting methods can be divided into the detection-based methods and the regression-based methods. The detection-based crowd counting methods usually use a sliding window to detect each pedestrian in the scene to obtain the approximate position of the pedestrian, and then count the number of pedestrians [5, 6, 7]. The detection-based methods can achieve good results for low-density crowd scenes, but are greatly limited for high-density crowd scenes. The incipient regression-based methods try to learn the direct mapping between the low-level features extracted from the local image blocks and the head count [8, 9, 10]. Such direct regression-based methods only count the number of pedestrians, while ignoring the important spatial information. Refs. [11, 12] suggested learning the linear or non-linear mapping between the local block features and their corresponding target density maps, which may incorporate the spatial information into the learning process. The great success of the Convolutional Neural Network (CNN) in many computer vision tasks motivated researchers to use CNN to learn nonlinear functions from crowd images to corresponding density maps or corresponding counts. In 2015, [PERSON] et al. [13] applied CNN to the crowd counting task firstly by using the Alexnet network structure [14]. The fully connected layer with 4096 neurons was replaced by a layer with only one neuron to count the number of pedestrians in the crowd image. In the same year, [PERSON] et al. [4] noticed that the performance of the existing methods was drastically reduced when they were applied to new scenes that differed from the training dataset. To overcome this problem, a data-driven method was proposed to fine-tune the pre-trained CNN model with the training samples similar to the density level in the new scene to adapt to the unknown application scenes. This method avoids the problem of re-training when the model is transformed to a new scene, but still needs a large amount of training data, and it is difficult to know the density level of the new scene in advance in practical applications. Inspired by the success of multi-column networks [15] in image recognition, in 2016, [PERSON] et al. [16] proposed a multi-column convolutional neural network-based architecture (MCNN) by constructing a network consisting of three columns of filters corresponding to the receptive fields with different sizes (large, medium, small) to adapt to changes of head size due to the perspective effects or image resolution. During training, each column of the MCNN pre-trains all image blocks, then combines the three networks for fine-tuning training. The training process is complex and there is a large amount of structural redundancy. In 2017, [PERSON] et al.