# 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 KITTI Data Set  
KITTI data set has given the relevant correction parameters for converting the camera coordinate system to the lidar coordinate system. Combining the rotation matrix \(R\) and translation matrix \(T\), the image tracking results can be associated with the LiDAR point cloud tracking results.  
When using KITTI data set for tracking simulation, the color image is converted to a binary image to simulate the intensity image generated by GM-APD LiDAR. Then, KCF and Kalman filter are used to track the image and point cloud data, respectively. The tracking scene is KITTI data set tracking scene 0001, and the tracking target is No. 86 white car. The motion state sequence of the car from unshielded to blocked by other cars to unshielded is selected. When tracking the target, PSLR and ISS need to be calculated to judge the occlusion state of the target. Finally, according to the occlusion state, the adaptive factor is used to effectively fuse the intensity image with the point cloud to track the position information. The calculation results are shown in the figure below.  
As can be seen from Figure 5, when the target is out of occlusion, the PSLR is 7.69 and the number of ISS key points is 9. When the target is partially obscured, the PSLR decreases to 5.62, and the number of ISS key points decreases to 6. When the target is in full occlusion, the PSLR is not 0 but drops to 1.77, and the number of ISS key points is 0. The results above all meet the threshold interval set in this paper when the target is completely occlusive, which proves that the occlusion threshold set in this paper is effective and meets the judgment of the subsequent tracking task on the target occlusion state. The tracking results are as shown partially in Figure 6:  
In Figure 6, the red box is the real position of the target, and the yellow box is the tracking position of the algorithm. It can be seen that when the target is in the unobstructed state, the kernel correlation filtering based on multi-feature fusion can effectively track the target of interest; when the target is partially occlusive, the tracking results based on the multi-feature fusion kernel correlation filtering are biased. Kalman tracking algorithm can predict the target position with a small error, but the target motion state is not completely linear. Extended Kalman Filter (EKF) [27], based on Kalman filter, uses nonlinear functions  
Figure 5: The results of PSLR and ISS in different occlusion.  
to predict and estimate the state. By using the nonlinear function in the equation of state and the observed equation, and performing the Taylor series expansion of the nonlinear function, keeping only the first-order term, the nonlinear model is approximated to a linear model, so it is more accurate than the Kalman estimate. The tracking accuracy diagram was drawn for nine consecutive frames from No. 86 white car that was never occluded to that in occluded and then gradually out of occluded, as shown in Figure 7. By combining kernel correlation and Kalman tracker, the tracking accuracy is better than Kalman filter and EKF when the target is in the unobserved state. When the target is partially and completely occluded, the high tracking accuracy is still maintained through adaptive factor adjustment. As can be seen from Table 1, the average CLE of the proposed algorithm is 21.67% higher than that of Kalman filter and 7.94% higher than that of EKF, which proves that the proposed method can accurately track targets in different occlusion states.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Kalman Filter** & **EKF** & **Proposed** \\ \hline Average CLE/m & 0.1509 & 0.1284 & 0.1182 \\ Average algorithm speed per frame/ms & 34 & 45 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average CLE of different algorithms.  
Figure 6: The comparation of different methods in different occlusion.