In recent years, the field of unmanned aerial vehicle (UAV) tracking has grown rapidly, finding numerous applications across various industries. While DCF-based trackers remain the most efficient and widely used in UAV tracking, recently lightweight CNN-based trackers using filter pruning have also demonstrated impressive efficiency and precision. However, the performance of these lightweight CNN-based trackers are still far from satisfactory. In the of generic visual tracking, emerging ViT-based trackers have shown great success by using cross-attention instead of correlation operation, enabling more effective capturing of relationships between the target and the search image. But to our knowledge, the UAV tracking community has not yet well explored the potential of vision transformers for more effective and efficient template-search coupling for UAV tracking. In this paper, we propose an efficient ViT-based tracking framework for real-time UAV tracking. Our framework integrates feature learning and template-search coupling into an efficient one-stream ViT to avoid an extra heavy relation modeling module. However, we observe that it tends to weaken the target information through transformer blocks due to the significantly more background tokens. To address this problem, we propose to maximize the mutual information between the template image and its feature representation produced by the ViT. The proposed method is dubbed TATrack. Additionally, to further enhance efficiency, we introduce a novel mutual information maximization-based knowledge distillation, which strikes a better trade-off between accuracy and efficiency. Exhaustive experiments on five benchmarks show that the proposed tracker achieves state-of-the-art performance in UAV tracking. Code is released at: https://github.com/xyyang317/TATrack.

Transformer-based models have improved visual tracking, but most still cannot run in real time on resource-limited devices, especially for unmanned aerial vehicle (UAV) tracking. To achieve a better balance between performance and efficiency, we propose AVTrack, an adaptive computation tracking framework that adaptively activates transformer blocks through an Activation Module (AM), which dynamically optimizes the ViT architecture by selectively engaging relevant components. To address extreme viewpoint variations, we propose to learn view-invariant representations via mutual information (MI) maximization. In addition, we propose AVTrack-MD, an enhanced tracker incorporating a novel MI maximization-based multi-teacher knowledge distillation framework. Leveraging multiple off-the-shelf AVTrack models as teachers, we maximize the MI between their aggregated softened features and the corresponding softened feature of the student model, improving the generalization and performance of the student, especially under noisy conditions. Extensive experiments show that AVTrack-MD achieves performance comparable to AVTrack’s performance while reducing model complexity and boosting average tracking speed by over 17%. Codes is available at https://github.com/wuyou3474/AVTrack.

Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.

In the edge computing environment, due to factors, such as expensive devices, complex scenarios, and fine control requirements, it is necessary to use simulators to construct heterogeneous devices, simulate task offloading scenarios, and evaluate and optimize decision-making algorithms. However, existing edge computing simulators overly simplify the simulation process, neglecting device interactions and task complexities, making it difficult to finely control the task offloading process and customize complex scenarios. We propose EdgeSim++, a realistic, versatile, and easily customizable edge computing simulator. EdgeSim++ has the capability to generate a batch of heterogeneous devices with various features, supporting the simulation of multilayer cloud-edge-end scenarios and various multiaccess edge computing architectures. It dynamically sets task and device resource attributes, enables devices to interact and schedule behaviors through the network, and facilitates easy customization of both machine learning and nonmachine learning offloading strategies. Additionally, EdgeSim++ supports network topology visualization and result visualization, providing intuitive displays of task transmission processes and offloading results, and real-time monitoring of device resource changes. To test the effectiveness of EdgeSim++, we provided default machine learning and nonmachine learning offloading strategies, detailed simulation steps, demonstrated scenario construction, showcased process control, monitored resource changes, and compared algorithm effects. The results illustrated that our simulator could realistically simulate complex edge computing scenarios, flexibly control decision processes, and customize complex offloading algorithms, exhibiting good scalability and reusability.