Aircraft tracking in infrared imagery with adaptive learning and interference suppression

Abstract

Airborne target tracking is a crucial part of infrared imaging guidance. In contrast to visual tracking tasks, the target in infrared imagery shows different visual patterns. Moreover, severe background clutter and frequent occlusion caused by infrared interference make it a challenging task. Recently, discriminative correlation filter (DCF)‐based trackers have shown impressive performance. However, the features adopted in DCF‐based trackers are either handcrafted or pre‐trained from a different task, which do not closely intertwine with the domain‐specific video. To settle this problem, it is proposed to make full use of online training to learn domain‐specific features. By integrating the correlation filter layer into the convolutional neural networks, the feature domain and the response maps of the DCF can be optimized iteratively in the initial frame. Meanwhile, utilizing the measurement of the response maps' peak strength, further adjustments to the feature domain can be made to achieve a sharper peak and suppress the interference region during the tracking process. Evaluations are conducted to prove the validity of proposed aircraft‐tracking algorithm.

AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

“…Liu [45] introduced a dual-network to combine deep semantic features with shallow local features, enhancing the network's capability for inter-class and intra-class discrimination. Wu [46] enhanced the tracking performance of the DCF base tracker for infrared aerial targets by integrating adaptive learning from the initial frame and interference suppression. Yuan et al [47] proposed a multi-feature fusion model to integrate manual features with deep features, enhancing the discriminative ability for TIR targets.…”