Infrared Small Target Tracking Algorithm via Segmentation Network and Multistrategy Fusion

To solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, and occlusion, this article proposes a multistrategy fusion tracking algorithm using an IR small target segmentation network (IRSTNet) as the detection head, which mainly includes six strategies: target pixel clustering, target feature threshold adjustment, large-area search, small-area tracking, gate tracking, and coordinate solution. First, candidate targets are obtained through the IRSTNet and pixel clustering strategy. Second, the range of candidate targets is further reduced through a threshold adjustment strategy. Then, real-time tracking of IR small targets is achieved through large-area search, small-area tracking, and wave gate tracking strategies. Finally, the longitude, latitude, and altitude of the tracked target are obtained through the coordinate calculation strategy. Both qualitative and quantitative experiments based on real IR small target sequences verify that our algorithm can achieve more satisfactory performances in terms of success rate, precision, and robustness compared with other typical visual trackers. In addition, we have deployed the tracking algorithm proposed in this study on the Orange Pi 5 embedded platform, and the tracking speed meets the real-time requirements.