# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 4 Experimental Procedure
### Discussion  
This paper proposes a precise tracking algorithm based on optical flow estimation for tracking weak targets in infrared image clusters. The algorithm addresses the technical problems of low recognition rates and poor tracking accuracy. Firstly, the principle of the proposed algorithm was derived and analyzed. The infrared image subspace was established according to the characteristics of the weak targets, and the maximum matching parameters of the weak target features were determined. A PigNet network structure based on the mask-CNN network structure was proposed, and the network structure was improved and optimized. Additionally, an SIR particle filtering target observation model was proposed, and the model-building process has been described in detail in the paper. This process projects the high-dimensional input image into the particle space, removes redundant information, and obtains the original image feature information. The target is segmented and labeled by the PigNet model. The target observation model determines the actual similarity between the target and the real state, modifies the particle weights, and estimates the target position and state accordingly. The motion of the respective pixel positions is determined by the improved optical flow estimation algorithm. The algorithm achieves high-accuracy tracking of clustered weak targets by utilizing the temporal variation and correlation of the pixel intensity data in the image sequence. Secondly, the experimental environment was constructed, and a large experimental dataset was acquired and shared on a web platform. This provides basic data for more research enthusiasts. The effectiveness of the proposed algorithm was verified using the experimental data obtained. Performance evaluation metrics of the popular target were used to analyze the proposed algorithm and compare it with currently popular algorithms. The final analysis concluded that the proposed algorithm ranked first in six out of seven evaluation metrics and that it had the best overall performance.  
Although satisfactory experimental results and an excellent performance evaluation have been achieved with the proposed algorithm, it is clear from the theoretical analysis that the structure of the algorithm needs to be further optimized and that the target recognition rate and tracking accuracy can be further improved by optimizing the network structure and setting the parameters. For the modeling of the infrared image subspace, if the relationship between the infrared radiation characteristics of the target and the particle state can be considered, the target recognition rate and recognition speed can be further improved. The algorithm proposed in this paper is based on a neural network structure. Optimizing the structure and parameters of the algorithm and implementing it for embedded system applications can further expand the application environment and space of the algorithm. Each enhancement space represents a target and motivation for further research in the future.  
The algorithm proposed in this paper can be applied to medical diagnosis, fire security, dangerous gas leakage, and other fields [30]; it can also be applied to military fields, such  
Figure 12: Statistical analysis chart of target tracking performance indicators. (**a**) Target identification graphs. (**b**) Tracking accuracy graphs.  
as target detection and the tracking of drones, missiles, aircraft, etc. [31]. The algorithm can also be applied to space debris detection and tracking to effectively protect satellites and space stations operating in space, so the in-depth study of the algorithm has certain value and significance.