Tracker Meets Night: A Transformer Enhancer for UAV Tracking

Abstract

Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of- the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize reliable UAV tracking at night, this work introduces the Transformer to the low-light enhancement and develops a brand new spatial-channel Transformer-based enhancer, namely SCT, which is trained in a novel tracking-specific manner and plugged prior to tracking approaches. To exploit global information that is crucial in low-light enhancement, the novel spatial-channel attention module is proposed to model long-range inter-independencies while preserving local context. In the enhancement process, SCT relights low-illumination images and alleviate noise through a robust non-linear curve projection. Moreover, to provide a comprehensive evaluation, we construct a large scale night tracking benchmark, namely DarkTrack2021, which contains 110 challenging sequences with 100K frames in total. Evaluations on both the public UAVDark135 benchmark and the newly constructed Dark- Track2021 benchmark show that the tracking-specific design enables SCT with significant performance gains for nighttime UAV tracking compared to other top-ranked low-light enhancers. Real-world tests on a typical UAV platform.

Publication
Submitted to IEEE Robotics and Automation Letters, 2022
Guangze Zheng
Guangze Zheng
Research assistant in Vision4Robotics

My research interests include deep learning, visual object tracking, and robotics.