Pyramid Attention Enhancement Network for Nighttime UAV Tracking

Abstract

Whilst Convolutional Neural Network (CNN)-based object tracking methods can achieve promising results on traditional well-lit datasets, it is challenging to accurately locate targets in low-light images taken in nighttime scenes, even for state-of-the-art (SOTA) trackers. Existing solutions often disregard potential image features beneficial for object tracking or focus solely on improving human perception, making it difficult to balance image enhancement and object tracking tasks. To address this issue and attain reliable nighttime unmanned aerial vehicle (UAV) tracking, we propose a lightweight Pyramid Attention-based low-light image enhancer, which serve as a plug-and-play solution before the trackers. In addition, we introduce a Pyramid Attention Module (PAM) to enhance the capability for multi-scale feature representation of images as image features are difficult to distinguish under low-light conditions. Experimental results reflect the effectiveness of our method in dealing with poor illumination situations.