# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 4 Experiments
### Results and Analysis  
As we can see from Table 2, the SASNet shows a more stabilized performance than the classical single column CNN method on the three datasets because the dual-column architecture can extract effectively multi-scale features of an image. Compared with the early multi-column CNN method, the proposed method also has significant performance, which is mainly because our method extracts and retains the important details of the image, reduces the layers of a network, and accelerates the speed of network convergence. To be specific, the performance of SASNet is better than most advanced methods in terms of RMSE on UCFCC_50. From all lists, the proposed SASNet attains a competitive result that achieves the lower MAE of 60.5 and RMSE of 101.3 on Part_A and the lower MAE of 184.3 and RMSE of 258.1 on the UCFCC_50 datasets. Specifically, it acquires the best MAE of 91.1 and RMSE of 169.6 on the UCFQNRF dataset. The significant improvements show that SASNet can effectively handle population heterogeneous distribution.  
Figure 3 displays the visual comparisons of different approaches on ShanghaiTech Part_A. From left to right, the images in the first column are some samples. The second column shows the ground truth by Manual annotation, and the third to fifth columns show the estimation value generated by MCNN, CAT-CNN, and the proposed SASNet, respectively. Through comparative analysis, the density map generated by this method in population heterogeneous distribution can retain detailed information well, especially when SASNet solves the problem of different scales in the process of crowd counting, and the estimated count of people is closer to the ground truth.  
In addition, to evaluate the feature representations of the proposed SASNet from diverse scenes with various crowd density scenes, we divided the ShanghaiTech Part_A test set into six groups according to the number of people in each scenario. Each set indicates a specific density level and the corresponding average number of people. We compared our method with two existing classic representative counting networks, MCNN and CSRNet. The MCNN performs well in the relatively sparse scenes, and the CSRNet performs well in crowded scenarios. However, the proposed HSRNet outperforms the two models in all groups, which further proves that our approach can generate more precise density maps in crowd heterogeneous distribution scenes. Figure 4 shows the histogram of the results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**ShanghaiTech Part_A**} & \multicolumn{2}{c}{**UCFCC\_50**} & \multicolumn{2}{c}{**UCF\_QNRF**} \\ \cline{2-7}  & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline MCNN [5] & 110.2 & 173.2 & 377.6 & 509.1 & 243.5 & 364.7 \\ Switching CNN [28] & 90.4 & 135 & 318.1 & 439.2 & 228 & 445 \\ CMTL [18] & 101.3 & 152.4 & 322.8 & 397.9 & 252 & 514 \\ CSRNet [19] & 68.2 & 115 & 266.1 & 397.5 & 120.3 & 208.5 \\ PCCNet [29] & 73.5 & 124 & 240 & 315.5 & 149 & 247 \\ TEDnet [30] & 64.2 & 109.1 & 249.4 & 354.5 & 113 & 188 \\ DSSINet [31] & 60.6 & 96 & 216.9 & 302.4 & 99.1 & 159.2 \\ MMNet [32] & 60.8 & 99 & 209.7 & 309.7 & 104 & 178 \\ HyGnn [33] & 60.2 & 94.5 & 184.4 & 270.1 & 100.8 & 185.3 \\ AMS-Net [34] & 63.8 & 108.5 & 236.5 & 319.2 & 86.5 & 167.2 \\ COMAL [35] & 59.6 & 97.1 & 231.9 & 333.7 & 102.1 & 178.3 \\ SASNet (Ours) & 60.5 & 101.3 & 184.3 & 258.1 & 91.1 & 169.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison Experiment on ShanghaiTech Part_A, UCF_CC_50, and UCF_QNRF datasets.