Tracking of small infrared targets based on convolutional neural network

Abstract

As an important application of computer vision, visual tracking has been studied more and more. However, there are still many problems to be solved for infrared target detection, especially for weak infrared target detection and tracking. In addition, convolutional neural networks are widely used in visual applications such as target detection, recognition, and tracking. Therefore, in this paper, an infrared weak target detection and tracking method is designed. Based on the criteria of visual saliency, the real target can estimate by calculating the Euclidean distance between the target and its neighbours. The detected position of the target is sent to a fully convolutional siamese network DeepCF for tracking. As we know, the factors such as lighting changes, scale changes and occlusion cause the tracking failure. Therefore, a tracking confidence T is proposed to judge whether the target is lost. The motion trajectory of the target is continuously estimated if it is lost. The experimental results show that the above method can effectively detect and track infrared weak targets.