# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 4 Experimental Procedure
### Analysis of the Experimental Results  
The objective of the infrared subspace is to extract information about the characteristics of the infrared target by representing two different parameter values of the infrared image information and \(\delta_{2}\) to achieve the extraction of feature information for the training of the PigNet network, providing the basic information. A modified PigNet network structure with 2000 image samples of different targets in different environments in the dataset was selected as the training set. To ensure no duplication of images between the validation and training sets, 1000 images from different dataset folders were selected as the validation set, so that 3000 images of the infrared target formed the overall image sample. The targets were annotated using Labelme software to produce the training dataset. The SIR particle filtering outputs the target box end position information through the change in target particles in the upper and lower frames of the \(W_{k}=(x,y,s)\) equation, providing the corresponding \(F_{b}\) and \(F_{k}\) parameter numbers of target position coordinates for PigNet network training. The pixel positions of the targets in each frame are labeled by the optical flow estimation algorithm in \(U(x,y)=H(x,y,t)\) for changes in target intensity information at different moments. Validating the algorithm proposed in this paper, the experimental analysis of the experimentally acquired dataset, as shown in Figure 6, shows the raw image information of multiple small targets acquired in long-wave infrared. Figure 5: Hardware equipment for airborne experimental platforms. (**a**) Airborne infrared platform. (**b**) On-board gondola. (**c**) Long-wave infrared detectors. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Set-Up** \\ \hline Color space configuration & HSV space \\ Component configuration & H component \\ Quantitative processing series & 64 \\ Kernel function & Epanachnekov \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specific configuration data of parameters. The fine-tracking effect of the algorithm reported in [5] for weak multi-targets in infrared images is shown in Figure 7. The algorithm explored in [5] can achieve the tracking of relatively small targets in the figure, but for weak targets, it cannot yet achieve the recognition and tracking of all the targets, as only part of the weak targets can be recognized and tracked. The fine-tracking effect for weak targets is not highly satisfactory. The pleasing tracking effect of the algorithm reported in [6] for weak multi-targets in infrared images is shown in Figure 8. From a comparison of Figures 7 and 8, it can be found that the algorithm of [6] is not as effective as the algorithm of [5] in terms of the number and accuracy of the multiple weak targets identified and can only identify and track some of the weak targets. Most of the weak targets in the infrared weak target fine-tracking video cannot be identified and tracked. It can be seen from the experimental results in Figure 9 that the algorithms reported in both [5,6] have the problem of target loss when tracking weak and small targets in infrared images. The reference algorithm can only track clear targets, and there are problems with target merging and tracking. In contrast, the proposed algorithm can track multiple weak and small targets in infrared images comprehensively and accurately, and the tracking accuracy is high, which shows that the algorithm designed in this study has ideal application performance. On this basis, taking the target tracking error as the test index, the tracking performances of the algorithms reported in [5,6] and the designed algorithm were tested with the increase in the number of images. The experimental results of the different algorithms are shown in Figure 10.  
Figure 6: Experimental infrared image sample. (**a**) Sample 1. (**b**) Sample 2. Figure 7: Fine-tracking effect of weak and small multi-targets in infrared images using the algorithm described in [5]. (**a**) Sample 1.