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, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches. To achieve semantic-level low-light enhancement targeting the high-level task, the novel spatial-channel attention module is proposed to model global information while preserving local context. In the enhancement process, SCT denoises and illuminates nighttime images simultaneously through a robust non-linear curve projection. Moreover, to provide a comprehensive evaluation, we construct a challenging nighttime tracking benchmark, namely DarkTrack2021, which contains 110 challenging sequences with over 100 K frames in total. Evaluations on both the public UAVDark135 benchmark and the newly constructed DarkTrack2021 benchmark show that the task-inspired design enables SCT with significant performance gains for nighttime UAV tracking compared with other top-ranked low-light enhancers. Real-world tests on a typical UAV platform further verify the practicability of the proposed approach. The DarkTrack2021 benchmark and the code of the proposed approach are publicly available at https://github.com/vision4robotics/SCT.
Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems
School of Computer Science and Engineering（计算机科学与工程学院）；University of Electronic Science and Technology of China（电子科技大学）；Department of Logistics and Maritime Studies（物流与海事研究系）；The Hong Kong Polytechnic University（香港理工大学）；Chengdu Union Big Data Tech Inc.（成都联合大数据科技有限公司）
Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to joint…
Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems. However, current CNN-based trackers can hardly generalize well to low-light scenes that are commonly lacked in the existing training set. In indistinguishable night scenarios frequently encountered in unmanned aerial vehicle (UAV) tracking-based applications, the robustness of the state-of-the-art (SOTA) trackers drops significantly. To facilitate aerial tracking in the dark through a general fashion, this work prop…
Low-light environments have posed a formidable challenge for robust unmanned aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since the potential image features are hard to extract under adverse light conditions. Besides, due to the low visibility, accurate online selection of the object also becomes extremely difficult for human monitors to initialize UAV tracking in ground control stations. To solve these problems, this work proposes a novel enhancer, i.e., HighlightNet, to light up potential objects for both human operators and UAV trackers. By employing Transformer,…
We introduce a camera pipeline for rendering visually pleasing photographs in low light conditions, as part of the NTIRE2022 Night Photography Rendering challenge. Given the nature of the task, where the objective is verbally defined by an expert photographer instead of relying on explicit ground truth images, we design an handcrafted solution, characterized by a shallow structure and by a low parameter count. Our pipeline exploits a local light enhancer as a form of high dynamic range correction, followed by a global adjustment of the image histogram to prevent washed-out results. We proporti…
MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learning
College of Computer Science and Engineering, Guilin University of Technology（桂林理工大学计算机科学与工程学院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）
Conditional Generative Denoiser for Nighttime UAV Tracking
School of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；University of Hong Kong（香港大学）
Dual Prompt-Driven Feature Encoding for Nighttime UAV Tracking
Pratt School of Engineering, Duke University（杜克大学工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；Shanghai Key Laboratory of Wearable Robotics and Human Machine Interaction, Tongji University（同济大学可穿戴机器人与人机交互重点实验室）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）
MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking
Cooperative Medianet Innovation Center（合作 medi 创新中心）；Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；CloudWalk Technology（云从科技）；Shanghai AI Laboratory（上海人工智能实验室）
Event-RGB Adaptive Tracking for Nighttime Highway Perception
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；School of Intelligence Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学智能科学与工程学院）
Visual object tracking, which is representing a major interest in image processing field, has facilitated numerous real world applications. Among them, equipping unmanned aerial vehicle (UAV) with real time robust visual trackers for all day aerial maneuver, is currently attracting incremental attention and has remarkably broadened the scope of applications of object tracking. However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers' capabilities to be deployed in the dark. In darkness, the conditions can be more complex and…
State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overlooking adverse atmospheric conditions such as fog, haze, smoke and dust. As a result, the robustness of trackers against these challenging conditions remains underexplored. To address this gap, we introduce physics-based volumetric fog simulation method for arbitrary MOT datasets, utilizing frame-by-frame monocular depth estimation and a fog formation optical model. We enhance …
RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and…
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Dept. of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Institute of Software Chinese Academy of Science（中国科学院软件研究所）
