Infrared Small Object Tracking Based on Att-Siam Network

Abstract

Aiming at the practical problems such as background clutter interference and occlusion during the tracking of small air targets by infrared imaging guided missiles, we propose an improved SiamFC infrared small target tracking algorithm. By mapping the original spatial data to a high-dimensional Hilbert space, the nonlinear mapping is implicit in the linear learner. This method has the advantages of learnability, efficient calculation, linearizability, and good generalization performance. The target tracking problem provides a new and effective approach with the actual needs of the application. We propose a Siamese web tracker (Att-Siam). Att-Siam fuses convolutional channel attention mechanism, stacked channel attention mechanism and spatial attention mechanism to improve tracking performance. Feature extraction of target objects is enhanced by fusing the channel attention mechanism and two convolution blocks at low convolutional layers. Compared with the benchmark algorithm SiamFC, the improved algorithm is tested on the infrared air weak and small target data set, and the tracking success rate and accuracy are increased by 32.3% and 20.9% respectively. The experimental results show that the proposed algorithm can adapt to complex and diverse infrared air scenes, and achieve effective and stable real-time tracking of small infrared air targets.