Most of the existing estimation methods for nonlinear systems have been developed by using non-self-error data (e.g., prediction error and observation error, etc.), potentially resulting in a tricky problem. In this paper, we propose a new estimator design for nonlinear Wiener-Hammerstein systems subject to quantised measurements, where the self-error data (i.e., initial error and estimation error) are used. For this purpose, the estimation error information is derived by introducing several auxiliary variables with an error feedback filter. Then, a compensated estimation error variable is designed to remove the hostile effect of the regressor matrix on the estimator. A novel adaptive parameter estimation learning law is proposed based on a performance evaluation function, where the compensated estimation error term, initial error term and several restraint conditions are used to construct the aforementioned evaluation function. In addition, the online verification of persistent excitation (PE) condition is also provided. Finally, the efficiency and availability of the proposed scheme are validated through numerical examples and experiment in comparison with the available estimation algorithms. Note to Practitioners—This study was motivated by the system modelling and identification problem of the servomechanism but is also uses to other nonlinear systems that have deadzone, saturation, backlash and hysteresis nonlinearity characteristics. Available methods to use common error data to establish an estimator that produces biased estimate and initial-value problems. This study introduces a new scheme using the self-error data to establish an estimator, to provide a new framework of identification method design, and to improve above-mentioned problems. The self-error data are directly related to parameter adaptive updates, thus giving positive estimation performance. In this study, we mathematically characterize the dynamical equations for the servomechanism. We introduce how to extract self-error data from the input and output data of system. This is the key step for us to construct an estimator in the future. Then, based on self-error data and several constraint conditions, a novel estimator is provided using recursive pattern. Preliminary practical experiments indicate that the proposed method is feasible but it has not yet been tested in the complex industrial production.

Most existing tracking methods try to represent the target by exploiting visual information as much as possible based on the various deep networks. However, the appearance model hardly describes the attribute feature of the target well, which makes the trackers fail to adapt to the complex visual surrounding. In this article, inspired by brain-like intelligence, we propose an One-stream Vision-Language Memory network (OVLM) for object tracking. Firstly, we use the combination of vision and language to build the target model and use the semantic information in the language to compensate for the instability of visual information, making the target model more stable in the face of complex appearance changes. Secondly, to build a more compact target model, we propose a memory token selection mechanism that utilizes linguistic information to eliminate tokens that do not contain target information. Furthermore, to provide better visual information for target modeling, we propose a language-based evaluation method to select high-quality target samples to be stored in the memory. Finally, OVLM achieves a 64.7% success rate on the large-scale tracking benchmark dataset TNL2K, outperforming the previous best result (VLT) by 11.6%. By exposing the possibility of the vision-language memory network, we aim to draw greater attention to it and open up new avenues for vision-language tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

Abstract Salp Swarm Algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm Algorithm, we proposed a Salp Swarm Algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm Algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm Algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm Algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame Optimization, Grasshopper Optimization, and Ant Lion Optimization.

Abstract At present, temporal and spatial contexts are widely used to improve the adaptability of a tracker. However, most existing methods usually focus on one aspect of the temporal or spatial context and rarely exploit them simultaneously. In this paper, a context‐aware Siamese Network (CSNet) is proposed, which skilfully integrates the modelling of temporal and spatial context into the Siamese tracking framework. Specifically, CSNet consists of a context‐based channel attention module and a context‐based cross‐attention module. The former aggregates spatial context information from different channels and dynamically emphasizes target features, which makes it easier for the tracker to distinguish the target from the background. The latter propagates the temporal context from the previous frames to the current frame to establish the part‐level relationship between the search region and the historical target state, which enables the tracker better adapt to the target deformation. In addition, to further mine context information, the CSNet is equipped with a state‐aware strategy to control the contribution of different context information in tracking. Extensive experiments on OTB2015, UAV123, GOT‐10k, LaSOT, and TrackingNet show that the proposed tracking method achieves comparable performance to the advanced trackers.

Abstract In order to solve the problem that the conventional tracker is not adapted to the abrupt motion, a tracking algorithm based on the improved salp swarm algorithm (ISSA) was proposed. Visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. Firstly, the adaptive mechanism of leader and follower is introduced into the original salp swarm algorithm (SSA) to balance the exploitation and exploration of the algorithm. This method can improve the accuracy and effect of tracking. Secondly, the golden‐sine algorithm was used to update the position of followers, considering that the SSA had a single spatial search mode for followers and was easy to fall into the local optimum. By comparing with 19 classical tracking algorithms, qualitative and quantitative analysis is carried out to verify the tracking effect of the proposed method. A large number of experimental results show that the algorithm proposed here has good performance in visual tracking, especially for mutation motion tracking.

Numerous tracking approaches attempt to improve target representation through target-aware or distractor-aware. However, the unbalanced considerations of target or distractor information make it diffcult for these methods to benefit from the two aspects at the same time. In this paper, we propose a target-distractor aware model with discriminative enhancement learning loss to learn target representation, which can better distinguish the target in complex scenes. Firstly, to enlarge the gap between the target and distractor, we design a discriminative enhancement learning loss. By highlighting the hard negatives that are similar to the target and shrinking the easy negatives that are pure background, the features sensitive to the target or distractor representation can be more conveniently mined. On this basis, we further propose a target-distractor aware model. Unlike existing methods of preference target or distractor, we construct the target-specific feature space by activating the target-sensitive and the distractor-silence feature. Therefore, the appearance model can not only represent the target well but also suppress the background distractor. Finally, the target-distractor aware target representation model is integrated with a Siamese matching network for visual tracking for achieving robust and realtime visual tracking. Extensive experiments are performed on eight tracking benchmarks show that the proposed algorithm achieves favorable performance.

Thermal sensors play an important role in intelligent transportation system. This paper studies the problem of RGB and thermal (RGBT) tracking in challenging situations by leveraging multimodal data. A RGBT object tracking method is proposed in correlation filter tracking framework based on short term historical information. Given the initial object bounding box, hierarchical convolutional neural network (CNN) is employed to extract features. The target is tracked for RGB and thermal modalities separately. Then the backward tracking is implemented in the two modalities. The difference between each pair is computed, which is an indicator of the tracking quality in each modality. Considering the temporal continuity of sequence frames, we also incorporate the history data into the weights computation to achieve a robust fusion of different source data. Experiments on three RGBT datasets show the proposed method achieves comparable results to state-of-the-art methods.

Ant lion optimiser (ALO) is a new nature‐inspired swarm intelligence optimisation algorithm that mimics the hunting mechanism of antlions in nature. ALO has been proved to have the merits of high exploitation and convergence speed benefiting from adaptive boundary shrinking mechanism and elitism. In this work, visual tracking is expressed as searching for object in whole search space by interaction between antlions and ants. A novel ALO‐based visual tracking framework is proposed and the adaptation and sensitivity of the parameters in ALO are discussed to improve tracking performance. In addition, considering that ALO tracker needs a lot of iteration consumption, kernel correlation filter with deep feature is integrated into the ALO tracking framework (ALOKCF) to improve track efficiency. Extensive experimental results prove that the ALO tracker is very competitive compared to other trackers, especially for abrupt motion tracking. At the same time, two visual tracking benchmarks are used to verify ALOKCF tracker achieves state‐of‐the‐art performance.

Visual tracking has recently gained a great advance with the use of the convolutional neural network (CNN). Usually, existing CNN‐based trackers exploit the features from a single layer or a certain combination of multiple layers. However, these features only characterise an object from an invariable aspect and cannot adapt to scene variation, which limits the performance of such trackers. To overcome this limitation, the authors study the problem from a new perspective and propose a novel convolutional layer selection method. To obtain robust appearance representation, they investigate the advantages of features extracted from different convolutional layers. To determine the correctness of the tracking prediction and updated model, they design a verification mechanism based on historical retrospect, which can estimate the deviation for each layer by bidirectionally locating the target. Meanwhile, the deviation works as the layer‐wise selection criteria. Extensive evaluations on the OTB‐2013, visual object tracking (VOT)‐2016 and VOT‐2017 benchmarks demonstrate that the proposed tracker performs favourably against several state‐of‐the‐art trackers.

Kernelised correlation filter (KCF)‐based trackers have recently attracted considerable attention due to their exciting accuracy and efficiency. Numerous improvements have been made later for coping with scales variation or partial occlusion etc . However, when there is an abrupt motion between the consecutive image frames, these trackers would face failure. To alleviate the problem, the authors present an extended cuckoo search (CS)‐based KCF tracker (called ECSKCF). At first, the extended CS algorithm is constructed by the Simplex method (SM). CS has obvious capability in global search while the SM has exceptional advantage in local search. Based on ECS method, motion prediction is transformed to globally search for optimal position intending to enhance the quality of base image. Then, combined ECS with Gaussian distribution, a hybrid motion model is introduced to KCF framework, which has the capability of capturing abrupt motion. Finally, a unified framework is designed to track smooth or abrupt motion simultaneously. Extensive experimental results in both quantitative and qualitative measures demonstrate the effectiveness of the authors’ proposed method for abrupt motion tracking.

Existing anomaly detection (AD) approaches rely on various hand‐crafted representations to represent video data and can be costly. The choice or designing of hand‐crafted representation can be difficult when faced with a new dataset without prior knowledge. Motivated by feature learning, e.g. deep leaning and the ability to directly learn useful representations and model high‐level abstraction from raw data, the authors investigate the possibility of using a universal approach. The objective is learning data‐driven high‐level representation for the task of video AD without relying on hand‐crafted representation. A deep incremental slow feature analysis (D‐IncSFA) network is constructed and applied to directly learning progressively abstract and global high‐level representations from raw data sequence. The D‐IncSFA network has the functionalities of both feature extractor and anomaly detector that make AD completion in one step. The proposed approach can precisely detect global anomaly such as crowd panic. To detect local anomaly, a set of anomaly maps, produced from the network at different scales, is used. The proposed approach is universal and convenient, working well in different types of scenarios with little human intervention and low memory and computational requirements. The advantages are validated by conducting extensive experiments on different challenge datasets.

This letter presents a novel visual tracking algorithm by using Incremental Non-negative Matrix Factorization (INMF) and dual ℓ1-norm constraints. Firstly, we introduce one ℓ1regularization into the NMF reconstruction, which enables appearance model to tolerate different noises to some extent. Meanwhile, we enforce another ℓ1regularization on the projection coefficients when using iterative operators to obtain NMF basis vectors for the effective tracking. Secondly, to obtain the sparse error and projection coefficient matrice, we present an iterative algorithm to solve the optimal problem, which ensures the representation is more robust. Finally, we take partial occlusion into construct likelihood function, and combined with INMF learning to update appearance model for alleviating tracking drift. Experimental results compared with the state-of-the-art tracking methods demonstrate the proposed algorithm achieves favorable performance when the object undergoes large occlusion, motion blur and illumination changes.

In order to give attention to the smooth distortion of two image mosaic evaluation indices concurrently, an image multiscale mosaic method based on fuzzy integral is proposed in this paper. Aiming at problems resulting from evaluating mosaic effect of single-factor evaluation index, the integrative evaluation method of the mosaic image based on synthesizing two independent single-factor indices is put forward by using a fuzzy integral which is largely subject to human feeling. According to the optimal image mosaic rules, an optimal weighted function is obtained. With the help of the optimal weighted function, wavelet coefficients are fused at different resolution levels. As a result, a smooth and seamless mosaic image is reconstructed. Finally, experiments are performed on the method presented in this paper.