While existing lightweight visual trackers can run in real-time at edge devices, they face the difficulty of object appearance changes. An effective solution to this problem is to add an online updatable dynamic template for trackers to learn about changes in target appearance over time. However, existing dynamic template utilization methods are unsuitable for lightweight networks, resulting in limited accuracy improvement and a significant increase in computational workload. In this paper, we propose PromptVT, an efficient and accurate video tracking framework, which consists of two important designs: a plug-and-play dynamic template prompter (DTP) and a hierarchical multi-scale transformer (HMT). The DTP module guides networks to effectively learn changes between initial and dynamic templates through two prompts without additional computational workload. The HMT module combines spatial features of the search area and template at different scales and levels, enabling the tracker to learn a more comprehensive visual representation. Our proposed PromptVT outperforms state-of-the-art real-time trackers on eight benchmarks (VOT2020, LaSOT, GOT-10K, UAV123, AntiUAV, AntiUAV410, TrackingNet, OTB100) while running at 52 fps (PyTorch model) and 76 fps (ONNX model) on CPUs, with only 2.9G FLOPs and 3M parameters. Code and models are available at https://github.com/faicaiwawa/PromptVT.

Existing underwater tracking methods can be categorized into two paradigms: first, “enhance-then-track”—first enhancing the quality of the input image, then employing an open-air tracker; second, “track-then-process”—initially using an open-air tracker, followed by calibrating the prediction box. These methods that lack unified objectives among the modules impair tracking performance. To overcome this, we propose a novel end-to-end framework called prompting underwater tracking (PUTrack). It adapts the open-air tracker to the scenario-specific (underwater) tracking task by deploying a set of underwater prompters at the lateral side of an existing open-air tracker, and injecting the generated prompts layer-by-layer into the encoder. Experiments on various underwater tracking datasets demonstrate that the method significantly improves the underwater performance of the tracker by introducing only 0.6 M trainable parameters (0.4% of total parameters). Moreover, to drive the development of underwater tracking, we construct a high quality underwater tracking dataset that is manually annotated with 139 k frames, which exceeds the total number of frames in previous underwater tracking datasets. It provides 90 test sets rich in challenge properties and 200 training sets of diverse kinds.

Maritime target detection under complex adverse weather conditions (e.g., fog, rain, and low light) is crucial for Unmanned Surface Vehicle (USV) navigation. However, achieving high detection accuracy and efficiency remains challenging due to coupled environmental interference and limited computing resources. In this paper, we propose W-MTD, a task-specific distillation framework designed for weather-robust and lightweight maritime target detection based on knowledge distillation. Building upon the Fine-grained Distribution Refinement (D-FINE) detection model, this method constructs a dual-path knowledge distillation framework tailored for maritime scenes. Through the synergistic optimization of feature similarity constraints and decoupled distillation, it facilitates multi-level knowledge transfer from a teacher model to a lightweight student model, mitigating feature degradation caused by model compression. A multi-scenario augmentation strategy is designed to balance convergence across different weather conditions. Experiments show that W-MTD’s student model improves detection accuracy by 7.0–13.9% under three adverse weather conditionscompared to the baseline teacher model trained solely on clear weather data while maintaining comparable clear-weather performance. With only 4 M parameters and 7 GFLOPs, the student model demonstrates favorable performance and efficiency compared to other real-time detectors, indicating its potential suitability for USV deployment.