# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 4 Experiment and Result Analysis
### Tracking Experiment Based on Data Collected by GM-APD LiDAR  
Because the GM-APD LiDAR developed by our research group is limited by the number of arrays, the imaging of vehicles and other targets detected by close-range targets is incomplete. In this paper, we first conduct target tracking experiments with a fixed field of view for 285m distant pedestrian targets to ensure that complete target information can be obtained each time of imaging. In the same way, the original image with 64x64 resolution will be reconstructed with super resolution first and then the tracking experiment will be carried out. After the detection of the initial frame point cloud, the size information of the bounding box of the tracking target will be saved. For the next frame point cloud data, the predicted position of Kalman filter will be taken as the origin and the size of the bounding box will be 1.2 times. The detection scene of tracking experiment is shown in Figure 8. A total of 50 frames of data are continuously selected for the tracking experiment, and the tracking target position of each frame is manually marked. The tracking results of selected sequences are shown in Figure 9. The tracking accuracy diagram was drawn for six consecutive frames to show the performance in the no occlusion scenario, as shown in Figure 10. The estimation results of different algorithms are shown in Table 2.  
Figure 8: The first outdoor detection scenario.  
Figure 7: CLE curve of different algorithms in the tracking experiment based on KITTI data set.  
As can be seen from the Figures 9 and 10, the kernel correlation filtering tracking algorithm based on intensity image can track the target effectively because the target is not blocked, and the tracking algorithm after fusion can also track the target effectively. As can be seen from Table 2, the average CLE is 18.76% higher than Kalman filter, and the accuracy is 9.41% higher than EKF. Because the cloud data of the detection field is less than that of KITTI dataset, the average time per frame of the algorithm in this paper is 33 ms.  
The target information in the above scenario is relatively complete. In order to further verify the performance of the proposed algorithm, the detection field of GM-APD LiDAR is fixed to detect and image the outdoor 600 m distant cross, and the point cloud data after the conversion of the intensity image and the range image generated by GM-APD LiDAR is followed up by tracking experiments. The target location of each scene is manually marked, and the detection scene is shown in Figure 11. The tracking results of selected sequences are shown in Figure 11. The tracking results of selected sequences are shown in Figure 12. The tracking results of selected sequences are shown in Figure 13. The tracking results of selected sequences are shown in Figure 14.  
Figure 12: Tracking results of the second outdoor detection scenario.  
As can be seen from Figure 12a, when the target is relatively complete in the intensity image, the kernel correlation filtering tracking algorithm can effectively track the target of interest. As the tracking target is blocked by the rear vehicle, as shown in Figure 12i, the tracking result based on the intensity image gradually fails, and the target position is predicted by Kalman filtering through adaptive factor adjustment. As can be seen from Table 3, the average CLE of the proposed algorithm in outdoor tracking scenes is 0.1542, which is 27.43% higher than that of Kalman filter and 14.19% higher than that of EKF. Because the detection data are affected by noise, the average processing time of each frame in this experiment is 39 ms.