Visual Target Tracking Research Articles

Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments

Accurate control of continuum robots in confined environments presents a significant challenge due to the need for a precise kinematic model, which is susceptible to external interference. This paper introduces a model-less optimal visual control (MLOVC) method that enables a tendon-sheath-driven continuum robot (TSDCR) to effectively track visual targets in a confined environment while ensuring stability. The method allows for intraluminal navigation of TSDCRs along narrow lumens. To account for the presence of external outliers, a robust Jacobian estimation method is proposed, wherein improved iterative reweighted least squares with sliding windows are used to online calculate the robot’s Jacobian matrix from sensing data. The estimated Jacobian establishes the motion relationship between the visual feature and the actuation. Furthermore, an optimal visual control method based on quadratic programming (QP) is designed for visual target tracking, while considering the robot’s physical constraint and control constraints. The MLOVC method for visual tracking provides a reliable alternative that does not rely on the precise kinematics of TSDCRs and takes into consideration the impact of outliers. The control stability of the proposed approach is demonstrated through Lyapunov analysis. Simulations and experiments are conducted to evaluate the effectiveness of the MLOVC method, and the results demonstrate that it enhances tracking performance in terms of accuracy and stability.

SFFN: Serial Feature Fusion Network for Visual Tracking

Abstract Recently, The Transformer tracker has excellent visual target tracking performance. The parallel arrangement of structural feature fusion networks proposed by the TransT tracker makes the Transformer learn the erroneous information. To address the issue, this paper proposes the SFFN tracker. It constructed a serial feature fusion network with the Transformer module in a serial interleaved arrangement to avoid learning erroneous information during the Transformer feature fusion process. The experiments show that SFFN performs well on the GOT-10k dataset, and it improves over TransT by 2.6%, 2.6%, and 2.3% in AO, SR0.5, and SR0.75, respectively.

Simulation of indoor space thermal energy circulation in architectural environment design detail features based on visual object tracking algorithm

The heat circulation of interior space plays a crucial role in the design of the built environment, affecting energy efficiency and residential comfort. With the development of science and technology, visual target tracking algorithm provides a new possibility for optimizing thermal energy cycle. The purpose of this paper is to study the simulation method of indoor space thermal energy cycle based on visual target tracking algorithm to improve the efficiency of thermal energy management in building environment design and ensure indoor comfort. The research uses laboratory Settings, combined with high-performance cameras and advanced visual target tracking algorithms, to monitor indoor heat source distribution and personnel movement in real time. Through data acquisition and analysis, the indoor heat cycle model was constructed and simulated by computational fluid dynamics (CFD) software to evaluate the influence of different design parameters on the heat cycle. Through the optimization of the design scheme, the efficiency of indoor heat use is significantly improved, and the comfort score of residents is significantly improved. Therefore, the simulation of indoor space thermal energy cycle based on visual target tracking algorithm provides an effective tool for building environment design, which can realize efficient energy management and improve living comfort.

MIMTracking: Masked image modeling enhanced vision transformer for visual object tracking

Recently, one-stage trackers achieve state-of-the-art tracking results due to more sufficient integration of search and template representations. These methods usually adopt an encoder for synchronous feature generation and interaction. Despite their high performance, the one-stage approaches tend to feed the encoder full input representations that are highly redundant and dense during training. With a focus on tackling this problem, a novel algorithm termed MIMTracking is developed for visual target tracking. MIMTracking exploits an encoder and a decoder for masked image modeling during training. Randomly sampled discrete search embeddings and template embeddings serve as input of the encoder. The lightweight decoder takes full representations as input and progressively highlights the target region. This design alleviates input redundancy and reduces the computational cost of the training process, thereby allowing for more efficient learning of useful representations. The proposed MIMTracking achieves state-of-the-art tracking results on numerous tracking datasets, e.g., 51.5 % area under curve (AUC) on LaSOText, outperforming the previous top tracker OSTrack by 2 %. Especially, our large model MIMTracking-L further improves the AUC to 53.4 % on LaSOText.

Visual Sensor Target Tracking and Localization Method for Automatic Excavators

Visual Sensor Target Tracking and Localization Method for Automatic Excavators

A novel concurrent learning-based fixed-time convergent visual depth observer for weakly persistently exciting perspective dynamical systems

The problem of synthesizing a fixed-time depth observer based on monocular image feedback is considered in this study. The key challenge lies in the fact that the perspective dynamical system used to model visual motion is typically characterized as being weakly persistently exciting, which complicates observer synthesis. Furthermore, the key to achieving the task objective (safe obstacle avoidance, for instance) lies in the synthesis of a depth observer that achieves rapid convergence of the (static) obstacle depth estimate to the ground truth in a known fixed-time. To address these challenges, and in contrast with prior schemes that rely on a motion-restrictive persistency of excitation (PE) condition for ensuring exponential convergence, a novel adaptive observer framework is considered in this study that incorporates a concurrent learning (CL) term for ensuring fixed-time observer convergence. In particular, the use of concurrent learning allows for the synthesis of a relaxed finite-time excitation condition that relies on historical data recorded over a dynamic sliding window in the recent past that the proposed observer relies on to ensure fixed-time convergence. Thus, a continuous-time reduced-order observer formulation is presented that relies on camera motion data to achieve fixed-time convergence to a uniform ultimate bound for a suitably large choice of the observer gains. Experimental results are used to demonstrate the efficacy of the proposed scheme in the presence of significant measurement noise. A performance comparison study is also undertaken to demonstrate superior performance of the proposed scheme compared to leading alternative designs. Finally, the practical applicability of the proposed scheme is verified by incorporating the proposed scheme within a reactive navigation scheme to accomplish obstacle avoidance. By incorporating a suitably informative CL term within the observer framework, the proposed scheme eliminates the need to rely on a difficult-to-verify PE condition, thus rendering it more suitable for practical applications like visual target-tracking and visual servo control.

Robust visual tracking via modified Harris hawks optimization

Due to its outstanding efficiency and high precision, Harris hawks optimization (HHO for short) is suitable for solving the problem of visual target tracking under conditions of occlusion, deformation, rotation and in other complicated tracking scenes. A visual target tracker based on HHO is proposed in this study. To further promote the efficiency and stability of the standard HHO method and reduce the probability of the iteration falling into local optima and algorithm prematurity, in this study we propose an improved method called Super-HHO and apply it to visual target tracking. Compared with standard HHO, Super-HHO is superior due to its parameter optimization and updating strategy. We first optimize the random parameters of HHO via chaos theory to avoid frequent repeated exploration of the feasible region. Next, we design a nonlinear renewal strategy for the escape energy, which solves the problem in traditional HHO in which the fixed escape energy cannot accurately reflect the real hunting process of Harris hawks. Mutation strategies are also designed for the locations of the prey and the hunters to improve the optimization ability and eliminate the risk of falling into local extremes. In addition, a frame scale adjustment method model is developed to address the the issue in which the use of a size-fixed tracking frame makes it easy to include too many invalid features, which reduces the efficiency. Finally, we use the OTB2015, and VOT2018 tracking evaluation datasets, which contain hundreds of visual sequences and more than 10 complex interference scenes to conduct a qualitative analysis, a quantitative analysis and a statistical analysis of ours and other classic trackers, and to effectively test and compare the success ratio, precision and stability of each tracker. The proposed method was also compared with other classic trackers using classic large-scale benchmarks such as LaSOT and TrackingNet. Experimental data prove that ours performs well in terms of robustness, precision and efficiency.

Efficient correlation information mixer for visual object tracking

Recently, Siamese tracking framework has been widely used in the visual tracking community. Siamese trackers usually use Cross-Correlation to aggregate templates and search information, so they realize the encoding of target information. However, the previous Cross-Correlation methods either ignore the object channel semantic information or ignore the object’s local information. This seriously limits the representation of embedded correlation features and reduces the performance of the trackers. In this paper, to solve these problems, we propose an effective correlation information mixer for visual target tracking. We design an information fusion network to efficiently aggregate template features and search features. In the information fusion network, we use Depthwise Cross-Correlation and Pointwise Cross-Correlation to extract the channel semantic information and local information of the object respectively, and use the correlation information mixer to fully fuse the two correlation maps to achieve the optimal target information encoding. Extensive experimental results show that our tracker achieves competitive performance compared with other state-of-the-art trackers on four benchmarks, including OTB, VOT, UAV123, and LaSOT.

Differences in left and right lower limb control strategies in coping with visual tracking tasks during bipedal standing.

Differences in motor control between the lower limbs may influence the risk of sports injury and recovery from rehabilitation. In this study, differences in the visual feedback ability of the left and right lower limbs were assessed using visual target tracking tasks. Thirty-four healthy young subjects (aged 20.4 ± 1.2 years) were asked to move their bodies back and forth while tracking a visual target displayed on a monitor in front of them for 30 s. The two target motions were sinusoidal (i.e., predictable patterns) and more complex (random) patterns. To assess the ability of the lower limbs to follow visual target tracking, antero-posterior CoP (right limb, CoPap-r; left limb, CoPap-l) and medio-lateral CoP (right limb, CoPml-r; left limb, CoPml-l) data were measured using a stabilometer. Tracking ability by visual feedback ability was calculated as the difference in displacement between the target signal and the trajectories of the right and left pressure centers as trapezoidal areas, and a smaller sum of area (SoA) over the entire measurement time was defined as a greater tracking ability. Regarding the SoA in the anterior-posterior CoP, the mean SoA in the sinusoidal and random tasks was significantly lower in the CoP-r data than in the CoP-l data, indicating that the right lower limb had a more remarkable ability to follow visual target tracking. Regarding the SoA in the medial-lateral direction (CoP), the mean SoA in the sinusoidal and random tasks did not significantly differ between the two legs. The right lower limb may have a tracking function activated by the target signal when responding to visual stimuli. Identifying the motor strategies of each lower limb in response to visual stimuli will not only help identify potential differences between each lower limb but also suggest the possibility of enhancing the role of each lower limb in balance control.

Exploration of agricultural IoT breeding tracking based on a saliency visual targettracking algorithm

Exploration of agricultural IoT breeding tracking based on a saliency visual targettracking algorithm

Dynamic visible light positioning based on enhanced visual target tracking

In visible light positioning systems, some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy, real-time performance, and robustness. However, there are still two problems: (1) When the captured LED disappears and the uncertain LED reappears, existing tracking algorithms may recognize the landmark in error; (2) The receiver is not always able to achieve positioning under various moving statuses. In this paper, we propose an enhanced visual target tracking algorithm to solve the above problems. First, we design the lightweight recognition/demodulation mechanism, which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the landmark. Then, we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning, when the receiver is under various moving statuses. Experimental results show that our system can achieve high-precision dynamic positioning and improve the system's comprehensive performance.

Improved TransT Target Tracking Algorithm Based on Hybrid Attention

The TransT method pioneered the introduction of the attention mechanism into the target tracking field, but there are still shortcomings in stability. To improve the stability of the algorithm, we propose an improved feature fusion algorithm for visual image target tracking, and we adopt the LKA attention mechanism to strengthen the local attention to the target to ensure the tracker achieves both global and local attention. The tracking of visual image targets is achieved. The experimental results show that the proposed method has a short execution time and high tracking accuracy.

RETRACTED ARTICLE: Digital development of sports industry based on mobile networks and visual target tracking

RETRACTED ARTICLE: Digital development of sports industry based on mobile networks and visual target tracking

Anti-Similar Visual Target Tracking Algorithm Based on Filter Peak Guidance and Fusion Network

Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference.

A Systematic Solution for Moving-Target Detection and Tracking While Only Using a Monocular Camera

This paper focuses on moving-target detection and tracking in a three-dimensional (3D) space, and proposes a visual target tracking system only using a two-dimensional (2D) camera. To quickly detect moving targets, an improved optical flow method with detailed modifications in the pyramid, warping, and cost volume network (PWC-Net) is applied. Meanwhile, a clustering algorithm is used to accurately extract the moving target from a noisy background. Then, the target position is estimated using a proposed geometrical pinhole imaging algorithm and cubature Kalman filter (CKF). Specifically, the camera’s installation position and inner parameters are applied to calculate the azimuth, elevation angles, and depth of the target while only using 2D measurements. The proposed geometrical solution has a simple structure and fast computational speed. Different simulations and experiments verify the effectiveness of the proposed method.

Robust visual tracking based on modified mayfly optimization algorithm

In this study, MOA is applied to visual target tracking for the first time, and a novel meta-heuristic tracking algorithm with efficiency and precision is obtained. Similar to the common problems of other classical swarm intelligence algorithms, standard MOA faces a high probability of falling into local extremals, early maturity and a low efficiency of a late convergence speed. Therefore, super-MOA, a modified MOA method, is proposed in this study. By designing the updating mechanism, the position and velocity parameters of ephemera are monitored and dynamically adjusted with the iteration degree, and the balance of the global and local optimization process in different iteration stages is improved. A mayfly progeny mutation strategy was proposed to alleviate the prematurity problem of the standard MOA algorithm. Meanwhile, we introduce a chaos algorithm to reconstruct the velocity parameter iteration mechanism, which alleviates the efficiency loss caused by the frequent repeated searching of historical locations by mayflies in standard MOA. In addition, we further understand and improve the parameters such as the dynamic gravitational coefficient and dance coefficient during the courtship flight of mayflies. We also design a frame size adaptive adjustment strategy, which effectively decreases the interference of invalid features. In terms of experiments, we compare this algorithm with other classical trackers from the qualitative, quantitative and statistical perspectives through OTB2015, VOT2018 and typical large-scale benchmarks. Sufficient tracking experiments in various tracking scenes show that our tracker performs great in terms of efficiency, robustness and accuracy.

Dynamic Learning Rate of Template Update for Visual Target Tracking

The trackers based on discriminative correlation filter (DCF) have achieved remarkable performance in visual target tracking in recent years. Since the targets are usually affected by various factors such as deformation, rotation, motion blur and so on, the trackers have to update the templates for tracking online. The purpose of template update is to adapt to the target changes, the magnitude of which is closely related to the motion state of the target. Actually, the learning rate of template update indicates the weight of the historical sample, and its value is fixed in most existing trackers, which will decrease the precision of the tracker or make the tracker unstable. In this study, a new dynamic learning rate method for template update is proposed for visual target tracking. The motion state of the target is defined by the difference in target center position between the frames. Then, the learning rate is adjusted dynamically according to the motion state of the target instead of the fixed value, which could achieve better performance. Experiments on the popular datasets OTB100 and UAV123 show that with the proposed dynamic learning rate for template update, the DCF-based trackers can improve tracking accuracy and obtain better tracking stability in scenarios such as fast movement and motion blur.

Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms

Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. Experiments were conducted on OTB100 and VOT2018 benchmark datasets, and the experimental data obtained were analysed to derive two visual single-target tracking algorithms with optimal tracking performance. Finally, the future development of tracking algorithms is envisioned.

A Biologically Inspired Appearance Modeling and Sample Feature-based Approach for Visual Target Tracking in Aerial Images

Visual tracking in uncrewed aerial vehicles is challenging because of the target appearance. Various research has been fulfilled to overcome appearance variations and unpredictable moving target issues. Visual saliency-based approaches have been widely studied in biologically inspired algorithms to detect moving targets based on attentional regions (ARs) extraction. This paper proposes a novel visual tracking method to deal with these issues. It consists of two main phases: spatiotemporal saliency-based appearance modeling (SSAM) and sample feature-based target detection (SFTD). The proposed method is based on a tracking-by-detection approach to provide a robust visual tracking system under appearance variation and unpredictable moving target conditions. Correspondingly, a semi-automatic trigger-based algorithm is proposed to handle the phases' operation, and a discriminative-based method is utilized for appearance modeling. In the SSAM phase, temporal saliency extracts the ARs and coarse segmentation. Spatial saliency is utilized for the object’s appearance modeling and spatial saliency detection. Because the spatial saliency detection process is time-consuming for multiple target tracking conditions, an automatic algorithm is proposed to detect the region saliences in a multithreading implementation that leads to low processing time. Consequently, the temporal and spatial saliencies are integrated to generate the final saliency and sample features. The generated sample features are transferred to the sample feature-based target detection (SFTD) phase to detect the target in different images based on samples. Experimental results demonstrate that the proposed method is effective and presents promising results compared to other existing methods.

Adaptive Spatial Regularization Target Tracking Algorithm Based on Multifeature Fusion

The accuracy and robustness of object-tracking algorithms are challenging tasks in the field of artificial intelligence. The discriminative correlation filter has fast tracking speed and target discrimination ability in visual target tracking, but it will be affected by unnecessary boundary effects. Although spatially regularized discriminative correlation filters (SRDCF) effectively solve this problem, it faces a slow tracking speed and cannot meet the real-time requirement. Moreover, the constraints added by this algorithm are fixed during the tracking process, which needs to reflect better the characteristics and appearance changes of a particular object. Therefore, we propose an adaptive spatial regularization target tracking model based on multifeature fusion and learn the spatial regularization weights by adaptive spatial regularization terms, so that the filter can adapt to the changes of the target, which can highlight the target area and suppress the background area more accurately. At the same time, in order to cope with the problem of target loss when the target is obscured by a large area or rotated to a large extent, texture features are introduced to improve the deficiencies of HOG features and CN features, so as to achieve the complementary advantages among different features. In addition, the benchmark algorithm SRDCF uses Gaussian–Seidel iterative method to solve the filter, which makes the tracking speed very slow and cannot be tracked in real time. Therefore, this paper proposes an ADMM (alternating direction method of multiplier) algorithm to optimize the solution of the filter and achieve the tracking real-time requirement. The experimental results on the OTB-2015 dataset show that the accuracy and success rate of the improved algorithm reach 86% and 81.9%, respectively. At the same time, in the OTB-2013 dataset, it has increased by 3.4% and 6.3%, respectively, on the basis of the benchmark algorithm. It is verified that the improved model not only improves the robustness of the algorithm but also can better adapt to the changes of the target, while effectively reducing the computational overhead and meeting the tracking real-time requirements.