# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 1 Introduction
[17] proposed switching the convolutional neural network for crowd counting (Switching CNN) to train the regressions by using a particular set of training data patches according to different  
Figure 1: Examples of crowded scenes. crowd densities in the image. The network consists of multiple independent CNN regressions, which is similar to a multi-column network, while a Switch classifier based on the VGG-16 [18] architecture was introduced to select the optimal regression for a particular input block. The Switch classifier and the independent regression are trained alternately. However, Switching CNN uses the Switch classifier to switch between the regressions, which is very expensive and often inaccurate. Similar to Refs. [16; 17], in 2017, [PERSON] et al. [19] considered that a single predictor in different scene environments is not sufficient to predict the number of pedestrian properly, and thus proposed a hybrid neural network Mixture of CNNs (MoCNN). The model structure consists of a mixture of expert CNNs and a gated CNN. The appropriate expert CNN is selected adaptively upon the background of the input image. In prediction, expert CNNs estimate the head count in the image, while gated CNN predicts the appropriate probability of each expert CNN. These probabilities are further used as weighting factors to calculate a weighted average of the head count estimated by all expert CNNs. MoCNN not only trains multiple expert CNNs, but also learns the probability of the head count estimated by each expert CNN through a gated CNN training. However, it can only be used for crowd counting estimation, while it cannot get the crowd density distribution information. [PERSON] et al. [20] proposed a low-rank and sparse-based deep-fusion convolutional neural network for crowd counting (LPCNN) by adopting a regression method based on low-rank and sparse penalty to promote the accuracy of the projection from the density map to global counting, which got an excellent performance. [PERSON] et al. [21] proposed scale-adaptive CNN (SaCNN) to estimate the crowd density map and integrate the density map to get a more accurate estimated head count by extracting feature maps from multiple layers and adapted them to have the same output size. [PERSON] et al. [22] combined convolutional neural network and Markov Random Field (CNN-MRF) to achieve the head count in static images which contained three parts: a pre-trained deep residual network 152 [23] to extract features, a fully connected neural network for count regress and a MRF to smooth the counting results of the local patches. In this way, high correlation of local adjacent patches was used to improve count accuracy. For the crowd counting task under high-density and complex environments, a feature fusion-based deep convolutional neural network method, FF-CNN (Feature Fusion of Convolutional Neural Network), was proposed in this paper to obtain more accurate crowd counting performance. FF-CNN aimed to map the crowd image to its crowd density map, and then obtained the head count by integration. Following MCNN [16], the geometry-adaptive kernels were adopted to generate high-quality density maps which were used as the training ground truths. VGG network was used as the trunk network of FF-CNN to obtain richer features. Deconvolution technique [24; 25] was used to achieve the fusion of high-level and low-level features. Two loss functions i.e., density map loss and absolute count loss, were used to jointly optimize for obtaining a more precise density map and a more accurate crowd count. In order to increase the sample diversity, the original images were cropped to images of 256 \(\times\) 256 with a random cropping method for each iteration.