Recently, Siamese trackers have gained widespread attention in visual tracking due to their exceptional performance. However, many trackers still suffer from limitations in challenging scenarios, such as fast motion and scale variation, which hinder the full exploitation of target features. Consequently, the accuracy and efficiency of the trackers are limited. Therefore, this paper proposes an efficient attention network, called EAN, to improve tracking performance. The EAN comprises three primary components, namely a Transformer-s subnetwork, a Transformer-t subnetwork, and a Feature-Fused Attention Module (FFAM). The designed Transformer-s and Transformer-t subnetworks adopt complementary structures and functions to fully integrate and emphasize the relevant feature information, including channel and spatial features. The FFAM is responsible for fusing the multi-level features from both subnetworks, which establishes the global dependencies between the templates and search regions and enhances the discriminative power of the model. To further improve the tracking accuracy, a novel Feature-Aware Attention Module (FAAM) is introduced into the tracking prediction head to enhance the feature representation capability of the model. Finally, we propose an efficient EANTrack tracker based on EAN for robust tracking in complex scenarios, which exhibits significant advantages in challenging attributes. Experimental results on multiple benchmarks indicate that our approach achieves remarkable tracking performance with a real-time running speed of 55.6fps.Note to Practitioners—Siamese trackers have garnered considerable attention in the field of visual tracking due to their impressive performance. However, these trackers often face limitations in challenging scenarios, which impede the complete exploitation of target features. As a result, the accuracy and efficiency of many trackers are compromised. To address these issues, we propose an efficient tracker called EANTrack to enable robust tracking in complex scenarios. Our EANTrack exhibits significant advantages in handling challenging attributes. Please refer to our complete paper for detailed information on the EANTrack tracker and experimental results. Practitioners in the field can benefit from our research by leveraging our findings and methodologies in their work. We encourage further exploration and experimentation to enhance the performance and applicability of visual tracking systems.

In complex environments, trackers are extremely susceptible to some interference factors, such as fast motions, occlusion, and scale changes, which result in poor tracking performance. The reason is that trackers cannot sufficiently utilize the target feature information in these cases. Therefore, it has become a particularly critical issue in the field of visual tracking to utilize the target feature information efficiently. In this article, a composite transformer involving spatiotemporal features is proposed to achieve robust visual tracking. Our method develops a novel toroidal transformer to fully integrate features while designing a template refresh mechanism to provide temporal features efficiently. Combined with the hybrid attention mechanism, the composite of temporal and spatial feature information is more conducive to mining feature associations between the template and search region than a single feature. To further correlate the global information, the proposed method adopts a closed-loop structure of the toroidal transformer formed by the cross-feature fusion head to integrate features. Moreover, the designed score head is used as a basis for judging whether the template is refreshed. Ultimately, the proposed tracker can achieve the tracking task only through a simple network framework, which especially simplifies the existing tracking architectures. Experiments show that the proposed tracker outperforms extensive state-of-the-art methods on seven benchmarks at a real-time speed of 56.5 fps.

In this article, we pay attention to develop an event-triggered adaptive neural network (ANN) control strategy for stochastic nonlinear systems with state constraints and time-varying delays. The state constraints are disposed by relying on the barrier Lyapunov function. The neural networks are exploited to identify the unknown dynamics. In addition, the Lyapunov-Krasovskii functional is employed to counteract the adverse effect originating from time-varying delays. The backstepping technique is employed to design controller by combining event-triggered mechanism (ETM), which can alleviate data transmission and save communication resource. The constructed ANN control scheme can guarantee the stability of the considered systems, and the predefined constraints are not violated. Simulation results and comparison are given to validate the feasibility of the presented scheme.

This article presents an adaptive fuzzy finite-time control (AFFTC) method for nonstrict-feedback nonlinear systems (NFNSs) with unknown dynamics. With the aid of the backstepping technique, by establishing the smooth switch function (SSF), a novel${C}^{1}$AFFTC strategy is recursively constructed, which counteracts the effect of nonstrict-feedback structure and unknown dynamics. Different from the reporting finite-time control achievements, the singularity hindrance derived from the differentiating virtual control law is availably surmounted. Moreover, the developed AFFTC strategy can drive the tracking error to converge into a small neighborhood of the origin in a finite time. Simulation results are conducted to substantiate the efficacy of theoretical findings.

In this article, an adaptive optimized control scheme based on neural networks (NNs) is developed for a class of perturbed strict-feedback nonlinear systems. An optimized backstepping (OB) technique is employed for breaking through the limitation of the matching condition. The disturbance of existing nonlinear systems may degrade system performance or even lead to instability. In order to improve the system's robustness, a disturbance observer is constructed to compensate for the impact coming from the external disturbance. Because the proposed optimized scheme needs to train the adaptive parameters not only for reinforcement learning (RL) but also for the disturbance observer, it will become more challenging no matter designing the control algorithm or deriving the adaptive updating laws. Finally, by virtue of the Lyapunov stability theory, it is proved that all internal signals of the closed-loop systems are semiglobal uniformly ultimately bounded (SGUUB). Simulation results are provided to illustrate the validity of the devised method.

Catadioptric panoramic image's application to computer visual field gains its popularity in recent years. However, due to its complicated imaging relationship, most existing mismatching elimination algorithms cannot directly operate on the unprocessed panoramic images. Those above algorithms usually need to unwarp the panoramic images before further processing. In order to solve the above problems, based on the distribution characteristics of features in the panoramic image, a novel mismatching elimination algorithm is proposed in this paper. Under different scene conditions, the novel algorithm can eliminate the mismatching features and improve the matching accuracy effectively. Experiments on the image databases confirm its effectiveness.