Visual tracking has made significant improvements in the past few decades. Most existing state-of-the-art trackers 1) merely aim for performance in ideal conditions while overlooking the real-world conditions; 2) adopt the tracking-by-detection paradigm, neglecting rich temporal contexts; 3) only integrate the temporal information into the template, where temporal contexts among consecutive frames are far from being fully utilized. To handle those problems, we propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently. Based on it, we propose a stronger version for real-world visual tracking, i.e., TCTrack++. It boils down to two levels: features and similarity maps . Specifically, for feature extraction, we propose an attention-based temporally adaptive convolution to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights. For similarity map refinement, we introduce an adaptive temporal transformer to encode the temporal knowledge efficiently and decode it for the accurate refinement of the similarity map. To further improve the performance, we additionally introduce a curriculum learning strategy. Also, we adopt online evaluation to measure performance in real-world conditions. Exhaustive experiments on 8 well-known benchmarks demonstrate the superiority of TCTrack++. Real-world tests directly verify that TCTrack++ can be readily used in real-world applications.

Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs). In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. One of the most promising frameworks for such a purpose is reinforcement learning. In this paper, a novel model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. The developed approach has been extensively tested with a quadcopter UAV in ROS-Gazebo environment. The experimental results show that our method is able to learn an efficient trajectory in a few iterations and perform actions in real-time. Moreover, we show that our approach significantly outperforms Q-learning based method. To the best of our knowledge, this is the first time that TEXPLORE has been developed to achieve autonomous navigation of UAVs.

Unmanned aerial vehicle (UAV) has facilitated a wide range of real-world applications and attracted extensive research in the mobile computing field. Specially, developing real-time robust visual onboard trackers for all-day aerial maneuver can remarkably broaden the scope of intelligent deployment of UAV. 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 harsh, easily posing inferior robust tracking or even tracking failure. To this end, this work proposes a novel discriminative correlation filter-based tracker with illumination adaptive and anti-dark capability, namely ADTrack. ADTrack firstly exploits image illuminance information to enable adaptability of the model to the given light condition. Then, by virtue of an efficient enhancer, ADTrack carries out image pretreatment where a target aware mask is generated. Benefiting from the mask, ADTrack aims to solve a novel dual regression problem where dual filters are online trained with mutual constraint. Besides, this work also constructs a UAV nighttime tracking benchmark UAVDark135. Exhaustive experiments on authoritative benchmarks and onboard tests are implemented to validate the superiority and robustness of ADTrack in all-day conditions.

Spatial regularization has been proved as an effective method for alleviating the boundary effect and boosting the performance of a discriminative correlation filter (DCF) in aerial visual object tracking. However, existing spatial regularization methods usually treat the regularizer as a supplementary term apart from the main regression and neglect to regularize the filter involved in the correlation operation. To address the aforementioned issue, this article introduces a novel object saliency-aware dual regularized correlation filter, i.e., DRCF. Specifically, the proposed DRCF tracker suggests a dual regularization strategy to directly regularize the filter involved with the correlation operation inside the core of the filter generating ridge regression. This allows the DRCF tracker to suppress the boundary effect and consequently enhance the performance of the tracker. Furthermore, an efficient method based on a saliency detection algorithm is employed to generate the dual regularizers dynamically and provide the regularizers with online adjusting ability. This enables the generated dynamic regularizers to automatically discern the object from the background and actively regularize the filter to accentuate the object during its unpredictable appearance changes. By the merits of the dual regularization strategy and the saliency-aware dynamical regularizers, the proposed DRCF tracker performs favorably in terms of suppressing the boundary effect, penalizing the irrelevant background noise coefficients and boosting the overall performance of the tracker. Exhaustive evaluations on 193 challenging video sequences from multiple well-known challenging aerial object tracking benchmarks validate the accuracy and robustness of the proposed DRCF tracker against 27 other state-of-the-art methods. Meanwhile, the proposed tracker can perform real-time aerial tracking applications on a single CPU with sufficient speed of 38.4 frames/s.

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 .

This paper aims to provide a clear explanation of the role of the footprint of uncertainty (FOU) parameters on the control signal generation and, thus, to increase the interpretability of specially structured interval type-2 (IT2) fuzzy logic controllers, namely single input IT2 fuzzy PID (SI-IT2-FPID) controller. In this context, we extend the analysis performed for SI-IT2-FPID controllers by providing the effect of the FOU parameters on control surface (CS) generation. We show that, by only adjusting a single parameter that shapes the FOU, it is possible to generate commonly employed CSs without a requirement of an optimization assistance. In order to validate our theoretical analysis, we present comparative real world quadcopter flight tests. The real-time experimental results show that the SI-IT2-FPID controller can achieve better control performance in the presence of uncertainties and strong wind conditions when compared to its type-1 and conventional counterparts. We believe that the results of this study will open the doors to a wider use of SI-IT2-FPID controllers in real world control applications as the proposed structure is easy to design and feasible to deploy, especially in real-time control systems.