# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 3 Experiments
### Results and Analysis  
The proposed method was compared with other existing methods and the experimental results were shown in Table 2. \"LBP + RR\" (local binary pattern + Ridge Regression) is a traditional regression-based method, which uses LBP features extracted from the original image as input and uses RR to estimate the head count for each image [4]. It can be seen that the method based on traditional artificial feature extraction is very ineffective for complex high-density crowd datasets, and the CNN-based method is far superior to the traditional method. Also, it can be concluded from Table 2 that the results of FF-CNN are much better than Cross-scene [4], MCNN [16], FCN [29], Cascaded-MTL [30] and Switching-CNN [17] method. Compared to LFCNN [20] and SaCNN [21], the proposed FF-CNN method shows lower error on the crowd-intensive Part A dataset. MAE and MSE for LFCNN are reduced by 7.45 and 3.1, respectively. MAE and MSE for SaCNN are reduced by 5.05 and 0.4, respectively. The difference is not obvious in the more sparse Part B datasets. MAE and MSE for our proposed method are only 1.75 and 0.79 higher, respectively, than those of LFCNN. MAE and MSE for our proposed method are only 0.25 and 0.39 higher, respectively, than those of SaCNN. However, LFCNN should go through two phases: deep-fusion density map regression and low-rankand sparse based regression. The FF-CNN proposed needs only one phase and the performance is excellent; SaCNN is more complex than the FF-CNN proposed in this paper. SaCNN needs more training parameters. Also, the resolution of the density map of SaCNN is only 1/8 of the original image, while the resolution of the density map obtained by FF-CNN is 1/4 of the original image. Compared to CNN-MRF [22], the proposed FF-CNN method shows relatively higher error on the crowd-intensive Part A, especially the error of MSE is higher than 8.7 which shows that CNN-MRF is more robust. On the Part B dataset, the proposed FF-CNN method performs better on MAE. The CNN-MRF contains three parts, a pre-trained deep residual network 152 to extract features, a fully connected neural network for count regress and a MRF to smooth the counting, which resulted in more parameters (residual network 152 contains more than 40 million parameters, our method contains about 12 million parameters) and calculations, but not much improvement. Figure 4 shows the ground truth head count and the estimated head count in the 182 test images of the Part A dataset. Figure 5 shows the ground truth head count and the estimated head count in the 316 test images of the Part B dataset. The dashed line indicates the ground truth head count, while the solid line indicates the head count estimated (the ground truth head count with the corresponding estimated head count is arranged in ascending order to get a more intuitive broken line diagram). It can be seen that either for Part A or Part B, the estimation head count is close to the ground truth head count. Note that for Part A, the crowd density ranges from 66 to 2255, while for Part B, the crowd density ranges from 11 to 539, which shows quite different density and varied density distribution of the crowd. However, the FF-CNN can be effectively adaptive to different density levels and the estimation results are very accurate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Part A**} & \multicolumn{2}{c}{**Part B**} \\ \cline{2-5}  & **MAE** & **MSE** & **MAE** & **MSE** \\ \hline LBP + RR [4] & 303.2 & 371.0 & 59.1 & 81.7 \\ Cross-scene [4] & 181.8 & 277.7 & 32.0 & 49.8 \\ MCNN [16] & 110.2 & 173.2 & 26.4 & 41.3 \\ FCN [29] & 126.5 & 173.5 & 23.76 & 33.12 \\ Cascaded-MTL [30] & 101.3 & 152.4 & 20.0 & 31.1 \\ Switching-CNN [17] & 90.4 & 135.0 & 21.6 & 33.4 \\ LFCNN [20] & 89.2 & 141.9 & 14.7 & 25.4 \\ SaCNN [21] & 86.8 & 139.2 & 16.2 & 25.8 \\ CNN-MRF [22] & 79.1 & 130.1 & 17.8 & 26.0 \\ \hline
**FF-CNN** & **81.75** & **138.80** & **16.45** & **26.19** \\ \hline \hline \end{tabular}
\end{table}