# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 4 Experiments
### Baseline  
In this study, ALFNet [29] and FCOS [49] were selected as the pedestrian region proposal networks. ALFNet [29] is a single-step detection model with a simple but effective structure. We extended the ALFNet model by incorporating additional modules, with the variations denoted as follows:  
1. ALFNet + visual: In this experiment, we added a visual feature module to the ALFNet model. 2. ALFNet + visual + pose: This experiment involved adding both a visual module and pedestrian pose modules to the ALFNet model. 3. ALFNet + visual + pose + attention: For this experiment, we incorporated the visual feature module, pedestrian pose modules, and attention mechanism into the ALFNet model. Ahelation study  
A series of ablation experiments were performed on the Caltech [17] dataset to verify the validity of each module in the model. Table 1 summarizes the detection loss rates of several models on the Caltech [17] dataset. The visual feature module reduced the loss rate of the detection model, such as the loss rates of ALFNet, FCOS (ResNet50), and FCOS (VoVNet39) from 5.13%, 4.68%, and 4.57% to 4.71%, 4.26%, and 4.13%, respectively. The highest accuracy was achieved for all models by further incorporating the pedestrian pose module and attention mechanism. Table 2 presents the statistics on the number of proposals, number of true positives, and number of false positives for all models. The original detection models generated a large number of candidate targets, i.e., 9459 for ALFNet and 17,823 for FCOS. The number of false positives was reduced, while the number of true positives was maintained by using an independent visual feature module. Taking FCOS as an example, the number of false positives decreased from 13,969 to 9080, whereas the number of correct targets only decreased by 155. This indicates that visual descriptions can increase detection accuracy. Furthermore, the number of proposals was substantially reduced by integrating the pedestrian pose information and attention mechanisms. We conducted extensive comparisons of our method with several methods on the CityPersons [12] dataset. Table 3 shows that the fusion of a visual attention mechanism and pose information led to improvements in detection performance across all evaluation subsets compared with that of the original model. Furthermore, Table 4 shows that the proposed approach effectively reduced the number of candidate targets. Table 3 indicates that the proposed method reduced the miss rate, regardless of the degree of occlusion. Moreover, the more severe the occlusion, the greater the performance improvement. For instance, for FCOS and ALFNet, an improvement of almost 3% was achieved. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & \multicolumn{2}{c}{**MR\({}^{-2}\) (\%)**} \\ \hline ALFNet & 5.13 & \\ ALFNet + visual & 4.99 & \\ ALFNet + visual + pose & 4.79 & \\ ALFNet + visual + pose + attention & 4.71 & \\ \hline FCOS(ResNet50) & 4.68 & \\ FCOS(ResNet50) +visual & 4.53 & \\ FCOS(ResNet50) + visual + pose & 4.39 & \\ FCOS(ResNet50) + visual + pose + attention & 4.26 & \\ \hline FCOS(VoVNet39) & 4.57 & \\ FCOS(VoVNet39) + visual & 4.41 & \\ FCOS(VoVNet39) + visual + pose & 4.27 & \\ FCOS(VoVNet39) + visual + pose + attention & 4.13 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The miss rate on the Caltech dataset.