# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### CityPersons Dataset
#### 4.3.2 Comparison with the State-of-the-Art Method of CityPersons  
Table 4 shows the comparisons with the state-of-the-art methods including Faster R-CNN [18], TLL [43], RepLoss [25], OR-CNN [26], ALF [6], CSP [1], Pedestron [42], and APD [28] on CityPersons. APD [28], OR-CNN [26], and RepLoss [25] are top pedestrian detection methods that focus on handling the occlusion problem. With the same ResNet-50 backbone, our proposed DAGN achieved 43.9% \(MR^{-2}\) which surpasses the second-best APD [28] (49.8%) by 5.9% in heavy occlusion cases. After being pretrained on the ECP, the performance of DAGN++ for heavy occlusion was 28.6% \(MR^{-2}\), outperforming the previous best (33.9%) by a margin of 5.3% in \(MR^{-2}\). For other cases, it also achieved competitive results. Figure 9 shows the visualization of the detection results of our method on the CityPersons validation set. One can find that several pedestrians missed by Pedestron [42] can be successfully detected by our proposed DAGN++.  
Figure 7: Quantitative comparison results of the Caltech testing set with five subsets: (**a**) Reasonable. (**b**) Heavy occlusion. (**c**) All. (**d**) Overlap\(-\)0.75. (**e**) Overlap\(-\)0.85.  
Figure 8: Example detection results of our proposed DAGN++ method compared with those of the Pedestron method of Caltech test set. (**a**) Pedestron [42] and (**b**) DAGN++ (ours). Green boxes, ground truth and red boxes, detection results. We visualized the detection boxes with the confidence score larger than 0.3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **R** & **H** & **P** & **B** & **S** & **M** & **L** & **Run Time (s/img)** \\ \hline Faster R-CNN [18] & VGG-16 & 15.4 & - & - & - & 25.6 & 7.2 & 7.9 & - \\ TLL [43] & ResNet-50 & 15.5 & 53.6 & 17.2 & 10.0 & - & - & - & - \\ RepLoss [25] & ResNet-50 & 13.2 & 56.9 & 16.8 & 7.6 & - & - & - & - \\ OR-CNN [26] & VGG-16 & 12.8 & 55.7 & 15.3 & 6.7 & - & - & - & - \\ ALF [6] & ResNet-50 & 12.0 & 51.9 & 11.4 & 8.4 & 19.0 & 5.7 & 6.6 & 0.27 \\ CSP [1] & ResNet-50 & 11.0 & 49.3 & 10.4 & 7.3 & 16.0 & 3.7 & 6.5 & 0.33 \\ APD [28] & ResNet-50 & 10.6 & 49.8 & 9.5 & 7.1 & - & - & - & 0.12 \\ APD [28] & DLA-34 & 8.8 & 46.6 & 8.3 & 5.8 & - & - & - & 0.16 \\ Pedestron [42] & HRNet & 7.5 & 33.9 & 5.7 & 6.2 & 8.0 & 3.0 & 4.3 & 0.33 \\ \hline DAGN (ours) & ResNet-50 & 11.9 & 43.9 & 12.1 & 7.6 & 18.7 & 5.8 & 5.9 & 0.22 \\ \hline DAGN++ (ours) & ResNet-50 & 8.4 & 28.6 & 7.0 & 5.6 & 9.2 & 2.4 & 5.6 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons with the state-of-the-art methods of the CityPersons validation set. R, reasonable; H, heavy occlusion; P, partial occlusion; B, bare; S, small scale; M, medium scale; and L, large scale. Red color values denote the best results.  
Figure 9: Example detection results of our proposed DAGN++ method compared with those of the Pedestron method on the CityPersons validation set. (**a**) Pedestron [42] and (**b**) DAGN++ (ours). Green boxes, ground truth and red boxes, detection results. We visualized the detection boxes with the confidence score larger than 0.3.