# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 1 Introduction  
The application of large numbers of drones forming drone swarms in the military, coupled with their increasing level of intelligence, has the potential to overturn the rules of future warfare. Drone swarms have the capability to accomplish complex tasks that are difficult to achieve with a single drone. However, the scale and complexity of drone swarms, resulting from the increase in the number of UAVs, also bring numerous security risks. These risks extend beyond mission-related factors and may pose a threat to national security and public safety. A swarm of UAVs represents one example of clusters of weak targets with distinct characteristics; thus, detecting, identifying, and tracking such clusters has become a technical challenge [1]. Among the challenges faced in this field are the low signal-to-noise ratio imaging of cluster weak targets in complex environments, where they tend to be submerged in complex backgrounds. Moreover, the uneven intensity of IR images further diminishes the recognition rates and tracking accuracy of multiple small targets [2]. Consequently, achieving fine target tracking in infrared images has become a pressing technical challenge. To address this challenge, this paper presents research on an accurate multi-target tracking algorithm for infrared images based on optical flow estimation. This algorithm aims to solve the problem of accurate tracking and recognition rates for weak and small targets in infrared image clusters. There are various problems in computer vision, including motion target recognition. However, image tracking techniques face challenges due to factors such as the complex uncertainty of the environment, background, and target, which can cause motion targets to become blurred and jittery during tracking. Moreover, when the target is far away from the detection system, it may appear as a point target or point source target, making itdifficult to detect and recognize due to the lack of spatial information, such as shape, size, dimension, and texture, as well as the similarity in motion characteristics between the target and decoy. This can lead to the loss or mistracking of the target. At the two moments of temperature change between day and night, the target cannot be detected or tracked due to the zero difference in radiation temperature between the target and the background [3]. To address these challenges, various studies propose different solutions. Reference [4] suggests enhancing feature extraction by increasing the degree of learning of edge features and using adaptive updating to determine the need for model updates in tracking filtered targets. However, the threshold-setting judgment of this method requires extensive experience and significantly affects the accuracy of the results. On the other hand, reference [5] presents a fast multi-domain convolutional neural network tracking algorithm that improves tracking accuracy by synthesizing features from multi-channel video. Nonetheless, this approach leads to increased processing time and poor real-time performance. In contrast, reference [6] introduces a multi-layer depth linear feature interpolation approach for training depth models through feature fusion. However, obtaining the depth model for this algorithm from a large number of image training sets is computationally intensive and time-consuming. In reference [7], a multi-layer convolutional feature-based method is proposed to calculate the correlation of multiple targets in a video image, which is then used for adaptive decision fusion in tracking. However, this algorithm requires combining the history information of multiple trackers and is computationally complex. Lastly, reference [8] proposes an improved RTMD Net tracking algorithm that divides multi-channel video images into different grid scales to enhance tracking accuracy. However, the formation of grid-scale information is complex and poses difficulties in information management.