The homography estimation of infrared and visible images, a key technique for assisting perception, is an integral element within the 6G Space–Air–Ground Integrated Network (6G SAGIN) framework. It is widely applied in the registration of these two image types, leading to enhanced environmental perception and improved efficiency in perception computation. However, the traditional estimation methods are frequently challenged by insufficient feature points and the low similarity in features when dealing with these images, which results in poor performance. Deep-learning-based methods have attempted to address these issues by leveraging strong deep feature extraction capabilities but often overlook the importance of precisely guided feature matching in regression networks. Consequently, exactly acquiring feature correlations between multi-modal images remains a complex task. In this study, we propose a feature correlation transformer method, devised to offer explicit guidance for feature matching for the task of homography estimation between infrared and visible images. First, we propose a feature patch, which is used as a basic unit for correlation computation, thus effectively coping with modal differences in infrared and visible images. Additionally, we propose a novel cross-image attention mechanism to identify correlations between varied modal images, thus transforming the multi-source images homography estimation problem into a single-source images problem by achieving source-to-target image mapping in the feature dimension. Lastly, we propose a feature correlation loss (FCL) to induce the network into learning a distinctive target feature map, further enhancing source-to-target image mapping. To validate the effectiveness of the newly proposed components, we conducted extensive experiments to demonstrate the superiority of our method compared with existing methods in both quantitative and qualitative aspects.

Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent advances in mobile edge computing have made it possible to offload computation-intensive object detection to edge servers equipped with high-accuracy neural networks, while lightweight and fast object tracking algorithms run locally on devices. This hybrid approach offers a promising solution but introduces a new challenge: deciding when to perform edge detection versus local tracking. To address this, we formulate two long-term optimization problems for both single-device and multi-device scenarios, taking into account the temporal correlation of consecutive frames and the dynamic conditions of mobile edge networks. Based on the formulation, we propose the LTED-Ada in single-device setting, a deep reinforcement learning-based algorithm that adaptively selects between local tracking and edge detection, according to the frame rate as well as recognition accuracy and delay requirement. In multi-device setting, we further enhance LTED-Ada using federated learning to enable collaborative policy training across devices, thereby improving its generalization to unseen frame rates and performance requirements. Finally, we conduct extensive hardware-in-the-loop experiments using multiple Raspberry Pi 4B devices and a personal computer as the edge server, demonstrating the superiority of LTED-Ada.