Joint feature embedding learning and correlation filters for aircraft tracking with infrared imagery

Infrared object tracking is a key technology for infrared imaging guidance. Blurred imaging, strong ego motion and frequent occlusion make it difficult to maintain robust tracking. We observe that the features trained on ImageNet are not suitable for aircraft tracking with infrared imagery. In addition, for deep feature-based tracking, the main computational burden comes from the feedforward pass through the pretrained deep network. To this end, we present an airborne infrared target tracking algorithm that employs feature embedding learning and correlation filters to obtain improved performance. We develop a shallow network and a contrastive center loss function to learn the prototypical representation of the aircraft in the embedding space. The feature embedding module is lightweight and integrated into the efficient convolution operator framework for aircraft tracking. Finally, to demonstrate the effectiveness of our tracking algorithm, we conduct extensive experiments on airborne infrared imagery and benchmark trackers. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduces an airborne infrared target tracking algorithm that utilizes feature embedding learning and correlation filters for improved performance. By developing a shallow network and a contrastive center loss function to learn the prototypical representation of the aircraft in the embedding space, and integrating the feature embedding module into the efficient convolution operator framework for aircraft tracking, the research achieves effective tracking of aircraft targets.