# Joint Pedestrian and Body Part Detection via Semantic Relationship Learning
## 4 Experiments
### Ablation Study  
In this section, we perform ablation experiments to analyze the impact of individual parts in the proposed approach, e.g., BPIF and AJ-NMS. We cover three ablation studies: (i) remove both BPIF and AJ-NMS from the proposed approach (denoted as Proposed w/o BPIF&A)-NMS), (ii) remove BPIF only (denoted as Proposed w/o BPIF), and (iii) remove AJ-NMS only (denoted as Proposed w/o AJ-NMS). All the experiments follow the same experimental setting as our full method. The results of all methods are shown in Figure 6 and Table 2.  
As shown in Figure 6 and Table 2, we can see that under most of the cases, removing either BPIF or AJ-NMS will lead to degraded detection performance. This indicates the usefulness of the proposed BPIF and AJ-NMS for the joint pedestrian and body part detection task. It also indicates that it is reasonable for the BiC and refinement modules to not share parameters.  
Compared to 'Proposed w/o BPIF&A)-NMS' and 'Proposed w/o AJ-NMS', 'Proposed w/o BPIF' and our full method have shown much better detection performance in head and pedestrian. The gain mainly comes from the improved detections for overlapped pedestrians by the proposed approach, and reduced false head detection by the proposed approach. Both are attributed to the designed BPIF and AJ-NMS. Specifically, with traditional NMS a false head detection may not be suppressed because its IOU with a nearby true head detection can lower the threshold to be suppressed. By contrast, the proposed AJ-NMS considers the head IOU together with the other three parts (head-shoulder, upper-body, and whole pedestrian). As a result, a false head detection can be suppressed with our AJ-NMS because the joint IOU of a false detection can be larger than the suppression threshold, and will be suppressed, leading to reduced false detections (high precision). Figure 7 gives an example in which a false head detection cannot be suppressed by the traditional NMS but can be suppressed by our AJ-NMS.  
Figure 5: Joint pedestrian and body part detection results by the proposed approach. Blue, white, red and green bounding boxes represent the detection results for pedestrian, upper body, head-shoulder, and head, respectively. The black lines indicate the body parts and pedestrian association information, i.e., referred four body parts belong to the same pedestrian.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Pedestrian AP** & **Upper-Body AP** & **Head-Shoulder AP** & **Head AP** \\ \hline Proposed w/o BPIF\&AJ-NMS & 94.1 & 92.8 & 90.8 & 86.0 \\ Proposed w/o BPIF & 94.6(\(\uparrow\)0.5) & 92.7(\(\downarrow\)0.1) & 90.8(\(\uparrow\)0.0) & 86.3(\(\uparrow\)0.3) \\ Proposed w/o AJ-NMS & 94.5(\(\uparrow\)0.4) & 93.0(\(\uparrow\)0.2) & 91.3(\(\uparrow\)0.5) & 86.6(\(\uparrow\)0.6) \\ Proposed & 94.9(\(\uparrow\)0.8) & 93.1(\(\uparrow\)0.3) & 91.4(\(\uparrow\)0.6) & 87.0(\(\uparrow\)1.0) \\ \hline \end{tabular}
\end{table}
Table 2: Ablation study of the proposed approach w.r.t. BPIF and AJ-NMS measured in terms of AP (in %).  
Figure 6: Ablation study of the proposed approach w.r.t. BPIF and AJ-NMS on the CUHK-SYSU Person Search Dataset measured in terms of recall vs. FPPI. (**a**) ROC curve for head detection, (**b**) ROC curve for head-shoulder detection, (**c**) ROC curve for upper body detection, and (**d**) ROC curve for the holistic pedestrian detection.