# LiDAR-Based Dense Pedestrian Detection and Tracking
## 3 Experimental Analysis
### Detection Performance Evaluation
To show the detection performance of the proposed method more clearly, the detection results of frame 50 are taken as an example, which is shown in Figure 9. The pedestrians in the scene are marked by a red box in Figure 9, which is used as ground truth. Figure 9 is the detection result of STM-KDE, and Figure 9 is the result of the proposed method. Note that the detection performance is analyzed in four different areas A, B, C, and D for a complete performance comparison. As shown in Table 2, there are five pedestrians in area A; one pedestrian is detected by STM-KDE, while all five pedestrians are successfully detected by the proposed method. There are four pedestrians in area B; three pedestrians are detected by STM-KDE, while all of them are successfully detected by the proposed method. One false detection appears in area C from STM-KDE. There are two pedestrians in Area D, both are successfully detected by STM-KDE, and one of them is detected by the proposed method. In the areas A and B where pedestrians are dense, five targets are missed by the STM-KDE. The origin point clouds of area A is given in Figure 10, and Figure 10 shows the detection results of proposed method. Note that the proposed method can provide a better segmentation and detection performance compared with STM-KDE, which is based on single template matching. In area C, there is a false target from STM-KDE. As shown in Table 1, there are many more false detections than the proposed method. The reason is the STM-KDE uses a single template to match targets, which have to set a lower threshold to adapt to the non-rigid shape of pedestrians. The proposed method has a missing alarm in area D, which is caused by the difference in point cloud proportion between pedestrians carrying backpacks and ordinary pedestrians. Thus, the extracted features exceeded the threshold, which caused the detection of this pedestrian to be invalid, while the STM-KDE detects the pedestrian. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Total Num** & **True Detection** & **False Detection** & **Precision** & **Recall** & **F1** \\ \hline STM-KDE & 2545 & 979 & 299 & 0.7660 & 0.3847 & 0.5122 \\ Proposed method & 2545 & **1659** & **34** & **0.9799** & **0.6519** & **0.7838** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pedestrian detection results on the 2011_09_28_drive_0016 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TP/FP** & **Area** & **B** & **C** & **D** \\ \hline Ground Truth & 5 & 4 & 0 & 2 \\ STM-KDE & 1/0 & 3/0 & 0/1 & 2/0 \\ Proposed method & 5/0 & 4/0 & 0/0 & 1/0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pedestrian detection results at frame 50. Figure 9: Comparison of pedestrian detection result between STM-KDE and proposed method at frame 50. (**a**) Ground truth. (**b**) Result of STM-KDE. (**c**) Result of proposed method. Table 4 shows the number of detections associated successfully, the number of trajectories, and the average survival time of the proposed method to the 0016 dataset. It can be seen from the table that the average survival time of the proposed method is longer than other methods. As the occluded trajectory can turn to matched trajectory as shown in Figure 2, it effectively reduces the rebirth and death caused by occlusion. This shows that the stability of the trajectory has been improved after adopting the new management strategy. To show the tracking performance of the new trajectory management strategy clearly, the tracking trajectories of the dataset 0016 and the Beihang data are shown in Figure 11 and Figure 12, respectively. Figures 11 and 12 show the trajectories with traditional strategy. Figures 11 and 12 show the trajectories with new strategy. These bold curves show the difference between the tracking trajectories obtained by