# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 4 Experiment
### Ablation Study on Match Methods  
CTC. As we all know, a bounding box is a rectangle surrounded by four coils. In this section, we used CTC to denote the coordinates of the top center point of the head and body bounding boxes. Because the head is generally located at the top and middle of the body, the CTC of most pedestrians' head bounding boxes should be very close to the CTC of their body bounding boxes, or even express the same pixel, as shown in Figure 8.  
LD and ED. To demonstrate the effectiveness of the IoC as an input weight for the association algorithm, we experimented with a variety of different weights. The location deviation (LD) of CTC coordinates of the two bounding boxes could be taken into account. LD maximizes confidences for head bounding boxes whereas those boxes are just above and centered on the body bounding boxes, as shown in (10), where \(loc\_dev\_x(*)\) and \(loc\_dev\_y(*)\) denote the location deviation between the body bounding boxes and the head bounding boxes from the x and y directions, respectively, and \(\alpha\) and \(\beta\) are hyperparameters. The Euclidean distance (ED) is a simple and crude measurement between two bounding boxes, as shown in (11), and this is used as the only calculation criterion for the degree of association. For LD and ED, the subsequent CostMatrix should also be changed, and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cut A** & **Patch** & **CutB** & **MOTA\(\uparrow\)** & **MOTP\(\uparrow\)** & **IDF\(\uparrow\)** & **Idsw\(\downarrow\)** & **HOTA\(\uparrow\)** \\ \hline \(-20\%\) & 5 px & 35 px & **55.07** & 78.55 & 52.18 & 4342 & **41.11** \\ \(-15\%\) & 5 px & 35 px & 55.04 & 78.54 & 52.16 & 4347 & 41.10 \\ \(-25\%\) & 5 px & 35 px & 55.06 & **78.56** & **52.19** & 4354 & 41.11 \\ \(-20\%\) & 0 px & 35 px & 54.99 & 78.56 & 52.15 & 4383 & 41.09 \\ \(-20\%\) & 10 px & 35 px & 55.09 & 78.53 & 52.18 & **4318** & 41.11 \\ \(-20\%\) & 5 px & 30 px & 54.81 & 78.55 & 52.04 & 4322 & 41.00 \\ \(-20\%\) & 5 px & 40 px & 55.02 & 78.54 & 52.16 & 4348 & 41.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The impact on TraPeHat when using different parameters to cut the body bounding boxes. After considering the two most important tracking indicators, MOTA and HOTA, the group parameters of \(\{-20\%\), five pixels, \(35\) pixels} performs best. (CutA: Ratios of pixels to cut off on left and right sides. Patch: Pixels patched to the top. CutB: Pixels kept at the bottom).  
Figure 8: All subplots in this figure come from the MOT20 dataset, in which the positions of the heads are demarcated by the yellow bounding boxes and the positions of the bodies are demarcated by the blue bounding boxes. A general rule can be concluded from this figure: pedestrians’ head bounding boxes are more likely to be located in the middle and upper positions of their body bounding boxes.  
specific details will not be repeated. In (10) and (11), the CTC points of the head and body bounding boxes are denoted as \(H_{i}\) and \(B_{j}\) for the purposes of convenient expression and understanding. We have  
\[LD(H_{i},B_{j})=\alpha*loc\_dev\_x(H_{i},B_{j})+\beta*loc\_dev\_y(H_{i},B_{j}) \tag{10}\]  
\[ED(H_{i},B_{j})=Eus\_dis(H_{i},B_{j}). \tag{11}\]  
The final results of the ablation study on match methods are shown in Table 2, from which it is not difficult to find that IoC achieves the best results. We speculate that the reason for this phenomenon is that IoC not only takes into account the distribution of the top center of the bounding box sets, but also reflects the extent to which the head bounding boxes are contained by the body bounding boxes, thus achieving the best results.