Set-point tracking servo systems encounter the problem of the trade-off between the swiftness and smoothness in tracking task. To deal with this problem, the proximate time-optimal servomechanism based on transition process (PTSTP) is proposed in this paper. The PTSTP control scheme incorporates a transition process (TP) into the framework of proximate time-optimal servomechanism (PTOS) to eliminate the conservatism of the original PTOS without the controller changing. The target position signal amplitude and the ultimate ability of actuator are utilized to design the time-optimal TP to make the jumping target position signal turns to a smooth signal, which can significantly reduce system overshoot. Therefore, the system swiftness and smoothness performance are guaranteed by PTSTP. Then, the stability of the proposed method is analyzed theoretically. Finally, the experimental results show that the controlled system is able to track the target position signal with different amplitude fast and smoothly in an electro-optical set-point tracking servo system.

In the realm of visual tracking, remote sensing videos captured by Unmanned Aerial Vehicles (UAVs) have seen significant advancements with wide applications. However, there remain challenges to conventional Transformer-based trackers in balancing tracking accuracy and inference speed. This problem is further exacerbated when Transformers are extensively implemented at larger model scales. To address this challenge, we present a fast and efficient UAV tracking framework, denoted as SiamPT, aiming to reduce the number of Transformer layers without losing the discriminative ability of the model. To realize it, we transfer the conventional prompting theories in multi-model tracking into UAV tracking, where a novel self-prompting method is proposed by utilizing the target’s inherent characteristics in the search branch to discriminate targets from the background. Specifically, a self-distribution strategy is introduced to capture feature-level relationships, which segment tokens into distinct smaller patches. Subsequently, salient tokens within the full attention map are identified as foreground targets, enabling the fusion of local region information. These fused tokens serve as prompters to enhance the identification of distractors, thereby avoiding the demand for model expansion. SiamPT has demonstrated impressive results on the UAV123 benchmark, achieving success and precision rates of 0.694 and 0.890 respectively, while maintaining an inference speed of 91.0 FPS.

Deep convolutional neural networks approaches often assume that the feature response has a Gaussian distribution with target-centered peak response, which can be used to guide the target location and classification. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produces sub-peaks on the tracking response map and causes model drift. In this paper, we propose a feature response regularization approach for sub-peak response suppression and peak response enforcement and aim to handle progressive interference systematically. Our approach, referred to as Peak Response Regularization (PRR), applies simple-yet-efficient method to aggregate and align discriminative features, which convert local extremal response in discrete feature space to extremal response in continuous space, which enforces the localization and representation capability of convolutional features. Experiments on human pose detection, object detection, object tracking, and image classification demonstrate that PRR improves the performance of image tasks with a negligible computational cost.