# LiDAR-Based Dense Pedestrian Detection and Tracking
## 3 Experimental Analysis
### Detection Performance Evaluation  
To validate the detection performance of the proposed method, the 2011_09_28_drive_0016 data in the KITTI dataset are used. After the preprocessing, such as the clustering and segmentation, we can use the three novel features to detect pedestrians. Figure 7a,b are, respectively, the pedestrian detection results after features-based discrimination of frame 1 and frame 38 under the parameters \(\theta_{thr}=0.35\), \(r_{t1l1}=1\), \(r_{t1r1}=20\), \(r_{t12}=1\), and \(r_{tr2}=50\). By comparing the fine segmentation clustering results in Figure 5c,f with the detection results in Figure 7a,b, it can be seen that the vehicle and traffic poles in the clustering results have been successfully removed. That means the three novel features defined in this paper can effectively distinguish pedestrians from other objects, such as vehicles, traffic poles, and trees. Figure 5: Clustering results of frame 1 and frame 38. (**a**) RBNN clustering result of frame 1. (**b**) Kernel density estimation of frame 1. (**c**) Fine segmentation result of frame 1. (**d**) RBNN clustering result of frame 38. (**e**) Kernel density estimation of frame 38. (**f**) Fine segmentation result of frame 38. Figure 6: Different descriptions of two very close pedestrians. (**a**) Optical image. (**b**) LiDAR point cloud. (**c**) Kernel density estimation result. The STM-KDE is used as the benchmark for the performance evaluation. In [15], kernel density estimation is used for pedestrian clustering and LARK features are extracted for pedestrian detection. The dataset is also used in [15] and pedestrian labels are available. In the dataset, there are 2174 pedestrians marked in 186 frames, but some of the partially occluded pedestrians are not marked. Based on the labels in [15], we additionally mark the partially occluded pedestrians and obtain 2545 pedestrians in 186 frames. Given by (30) the accuracy rate, recall rate, and F1 score are used to measure the detection effect. \[\text{P = \frac{\text{TP}}{\text{TP}+\text{FP}}\quad\text{R = \frac{\text{TP}}{\text{TP}+\text{FN}}\quad\text{F1 = \frac{2\text{PR}}{\text{P}+\text{R}}}} \tag{30}\]  
where TP is the number of true positives, FP is the number of false positives, FN is the number of false negatives, P and R are the accuracy rate and recall rate, respectively, and the F1 score is the harmonic average of P and R. The F1 score is widely used as the performance evaluation index, which means that the higher the F1 score is, the better the detection performance of the method is. Table 1 shows the detection results of the STM-KDE and the proposed method on the 2011_09_28_drive_0016 dataset. It can be found that the proposed method not only improves the detection accuracy, but also significantly improves the recall rate of pedestrian detection. The F1 score is also improved from 0.5122 to 0.7838. Figure 8 shows the ROC curves of different detection methods. The blue line shows the performance of the proposed method. The red line shows the performance of the STM-KDE. It can be seen that, under the same false-alarm number, the detection rate of the proposed method is higher than STM-KDE. The point cloud from long-distance pedestrian is sparse. STM-KDE uses the unique template based on LARK feature to match targets, which is difficult to use effectively for pedestrians at different distances. Perhaps using multiple templates from different distances could improve the detection performance of STM-KDE. In the proposed method, the three novel features are related to the proportion of pedestrian body, so they are robust to different distances and can obtain better detection performance. Figure 8: ROC curves of different methods for pedestrians detection. Figure 7: Pedestrian detection results of frame 1 and frame 38 after features-based discrimination. (**a**) Frame 1. (**b**) Frame 38.