Virtual Trajectory Research Articles

Research on Cattle Behavior Recognition and Multi-Object Tracking Algorithm Based on YOLO-BoT.

In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring.

Practical fixed-time non-singular sliding mode control of second order nonlinear dynamic systems with chattering and overshooting avoidance

This paper introduces a Practical Fixed-Time Stabilization technique (PFxT) designed for a specific class of nonlinear second-order systems. The closed-loop systems, when appropriately parameterized, exhibit convergence within a predetermined time frame to a confined region near the origin. This outcome is achieved by amalgamating a nonlinear sliding mode approach with a practical fixed-time tracking virtual trajectory. The control algorithmś design incorporates considerations for overshoot reduction and chattering cancellation. Furthermore, the Lyapunov method is employed to establish the practical fixed-time stability of the proposed solution, providing insights into the limits within which the algorithm can be effectively deployed. To complete the algorithm specification, a parameter selection approach is introduced, enabling customization of the desired settling time. Comprehensive simulations are conducted to validate the effectiveness and viability of the proposed PFxT technique.

Research of integrated spiral maneuvering and guidance based on virtual target

To address the issue of interception, maneuvering, and precise guidance during the final stage of gliding flight, a study was conducted on the design of integrated maneuvering and guidance using a spiral trajectory as the focus. Firstly, the spiral trajectory was decomposed into an optimal reference trajectory considering the glide angle constraint and a relative motion trajectory. Then, virtual circular moving targets were introduced to meet the requirements of spiral maneuvering and virtual trajectory moving targets were introduced to ensure trajectory convergence, based on the task division of different flight stages. Finally, the guidance rate of the reference trajectory was designed based on optimal control theory, and the guidance rate of the spiral maneuver was designed based on pure proportional guidance. Simulation results show that this guidance method can enable the vehicle to perform active spiral maneuvers, and control the landing deviation and impact angle deviation to within 2.5 m and 1 degree, respectively.

The Influence of Radiographic Parameter on the S2 Alar-Iliac Screw Virtual Trajectory in Degenerative Lumbar Scoliosis Patients: A Computed Tomography Study.

S2 alar-iliac (S2AI) screw had been widely used in the pelvic fusion for degenerative lumbar scoliosis (DLS) patients. However, whether S2AI screw trajectory was influenced by sagittal profile in DLS patients had not been comprehensively investigated. The objective of this study was to evaluate the associations between the optimal S2 alar-iliac (S2AI) screw trajectory and sagittal spinopelvic parameters in DLS patients. Computed tomography (CT) scans of pelvis were performed in 47 DLS patients for three-dimensional reconstruction of S2AI screw trajectory from September 2019 to November 2021. Five S2AI screw trajectory parameters were measured in CT reconstruction images, including: 1) angle in the transverse plane (Tsv angle); 2) angle in the sagittal plane (Sag angle); 3) maximal screw length; 4) screw width; and 5) skin distance. The lumbar Cobb angle, lumbar apical vertebral translation (AVT); global kyphosis (GK); thoracic kyphosis (TK); lumbar lordosis (LL); sagittal vertical axis (SVA); sacral slope (SS); pelvic tilt (PT); and pelvic incidence (PI) were measured in standing X-ray films of the whole spine and pelvis. Both Tsv angle and Sag angle had significant positive associations with SS (p < 0.05) but negative associations with both PT (p < 0.05) and LL (p < 0.05) in all cases. Patients with SS less than 15° had both smaller Tsv angle and Sag angle than those with SS equal to or more than 15° (p < 0.05). The decreased LL would lead to the backward rotation of the pelvis, resulting in a more cephalic and less divergent trajectory of S2AI screw in DLS patients. For DLS patients with lumbar kyphosis, spine surgeons should avoid both excessive Tsv and Sag angles for S2AI screw insertion, especially when using free-hand technique.

Browsing target extraction and spatiotemporal preference mining from the complex virtual trajectories

Public Map Service Platforms (PMSPs) aggregate and disseminate the earth observation data. Leveraging spatiotemporal preference patterns derived from browsing targets within complex virtual trajectories on PMSPs aids in constructing user-profiles and comprehending their intentions. However, complex virtual trajectories, characterized by numerous trajectory points and overlapping pyramidal spatial structures, introduce inefficiencies and inaccuracies during browsing target extraction. To mitigate this, we propose an Optimized Spatial Structure Segmentation (OSSS) method that divides complex virtual trajectories into sub-trajectories with simplified spatial structures, enhancing the efficiency of browsing target extraction. Spatiotemporal reconstruction of these sub-trajectories establishes sequences of browsing targets, revealing patterns of interest transitions. Moreover, recognizing the spatial uncertainty inherent in complex virtual trajectories, which results in imprecise matching between browsing targets and spatial features, we introduce a spatial co-occurrence semantic modeling approach. This involves constructing a POI semantic space and introducing a semantic similarity matching method to reduce spatial uncertainty and refine the accuracy of mining spatiotemporal preference patterns. We evaluate these methods using real-world data from Tianditu. Results demonstrate that the OSSS improves extraction efficiency by 3.45 times and accuracy by 18.84%. Additionally, the semantic similarity approach combined with spatial co-occurrence effectively mitigates spatial uncertainty. This research contributes to advancing the intelligence of PMSPs.

Formation control of UAV–USV based on distributed event-triggered adaptive MPC with virtual trajectory restriction

This paper studies the formation and trajectory tracking problem of unmanned aerial vehicle-unmanned surface vehicle (UAV–USV). A distributed event-triggered adaptive model predictive control (DEAMPC) method is constructed for the formation control by using information of the adjacent vehicles. First, the preset formation equation and parameter change are incorporated into the construction process of the cost function to achieve the formation control. Second, in order to reduce the calculation times of the optimization problem, an event-triggered mechanism is proposed. Finally, the virtual trajectory restriction is designed to deal with the condition of interrupted communication network. Moreover, some simulation results are given to illustrate the advantages and effectiveness of all the above methods.

Image reconstruction from truncated fan-beam projections along a circular trajectory using the virtual fan-beam method

Objective. In this paper, we investigate how the virtual fan-beam (VFB) method can be used to perform mathematically correct 2D reconstruction in a region-of-interest (ROI), using truncated fan-beam projections acquired on a circular scan, for truncation that only occurs on one side of the object. Approach. We start by choosing a virtual fan-beam trajectory and specifying how to obtain the corresponding virtual projections. Then, three VFB formulas are obtained by applying known super-short-scan (SSS) formulas to this virtual trajectory. Two of them perform the backprojection in a virtual parallel geometry and the third in the virtual fan-beam geometry. Next, we develop two VFB formulas that perform the backprojection step in the fan-beam acquisition geometry. Main results. We present five VFB reconstruction formulas for this truncation setting. To our knowledge, the two VFB formulas performing the backprojection in the fan-beam acquisition geometry are new. Moreover, the five VFB formulas presented here obtain accurate reconstruction in a larger ROI than what has been previously reported in the literature in the same setting. A complete mathematical derivation of these five VFB formulas is given, and their implementation is described step by step. Numerical simulations, using the Forbild head and thorax phantoms, demonstrate the efficacy of these formulas. A spatial resolution analysis and a variance study indicate minor differences between these five VFB formulas. Significance. This work shows that many different VFB formulas can be applied to perform mathematically correct 2D reconstruction in a ROI, in case of truncated fan-beam projections acquired on a circular scan. Moreover, the two new VFB formulas, with backprojection in the acquisition geometry, may open the path for an extension of the VFB method to 3D reconstruction from transversely truncated cone-beam projection acquired on a circular scan.

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities.

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k- 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k-1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.

A System Based in Virtual Reality to Manage Flood Damage

Abstract: Virtual Reality is seen as the high-end of human-computer interactions and it has the potential to target a wide range of applications. During the flood, it is difficult to communicate with the people by the rescue team from the helicopter.It is not possible to interact with Everyone directly. Because there won’t be any mode of communication during the disaster.The VR system helps Communicate with the people. Redirected walking algorithms require the prediction of human motion in Order to effectively steer users away from the boundaries of the physical space. While a virtual trajectory may be represented using straight lines connecting waypoints of interest, this simple model does not accurately represent typical user behavior. We implemented the model within a framework that can be used for redirect walking within different virtual and physical environments. It Is useful For the evaluation of redirected of parameters under varying conditions. In the proposed system a projection is made from the helicopter to the ground.When a person stands on a specified location, that particular message is Gathered. This helps to communicate with the affected people

Novel learning framework for optimal multi-object video trajectory tracking

BackgroundWith the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. MethodsTo implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky–Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. ResultsIn addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. ConclusionsFurther comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.

Angulation error assessment for the trajectory in the anteroposterior and lateral fluoroscopic views during percutaneous endoscopic transforaminal lumbar discectomy

BackgroundAnteroposterior (AP) and lateral fluoroscopies are often used to evaluate the intraoperative location and angulation of the trajectory in percutaneous endoscopic transforaminal lumbar discectomy (PETLD). Although the location of the trajectory shown in fluoroscopy is absolutely accurate, the angulation is not always reliable. This study aimed to evaluate the accuracy of the angle shown in the AP and lateral fluoroscopic views.MethodsA technical study was performed to assess the angulation errors of PETLD trajectories shown in AP and lateral fluoroscopic views. After reconstructing a lumbar CT image, a virtual trajectory was placed into the intervertebral foramen with gradient-changing coronal angulations of the cephalad angle plane (CACAP). For each angulation, virtual AP and lateral fluoroscopies were taken, and the cephalad angles (CA) of the trajectory shown in the AP and lateral fluoroscopic views, which indicated the coronal CA and the sagittal CA, respectively, were measured. The angular relationships among the real CA, CACAP, coronal CA, and sagittal CA were further demonstrated with formulae.ResultsIn PETLD, the coronal CA is approximately equal to the real CA, with a small angle difference and percentage error, whereas the sagittal CA shows a rather large angle difference and percentage error.ConclusionThe AP view is more reliable than the lateral view in determining the CA of the PETLD trajectory.

Adaptive distributed observer-based predictive formation tracking control for autonomous underwater vehicles

Numerous existing autonomous underwater vehicles (AUVs) formation control works have focused on the observation of external time-varying disturbances in trajectory tracking control. However, external time-varying disturbance outliers cannot be ignored due to the complex marine environment. It is rarely considered in previous research. In this paper, a double-layer distributed formation prediction control scheme is designed, including an upper-level adaptive distributed observer and a lower-level predictive controller. Firstly, the velocity is observed and regarded as an unknown variable by the expectation maximization algorithm. The gain in the observer will be optimized by adaptive cubature Kalman to improve the anti-disturbance performance of the adaptive distributed observer. Then, the constraints of internal safety distance and input saturation are fully considered. The distributed cooperative cost function is constructed based on the virtual trajectory, which is further introduced into local rolling domain optimization to realize formation predictive control. The boundedness of innovation and Lyapunov stability are utilized to prove the reliability of the control system. Some comparative simulation experiments are designed to demonstrate the effectiveness and robustness of the proposed method.

Neuro-adaptive control for searching generalized Nash equilibrium of multi-agent games: A two-stage design approach

This paper investigates the generalized Nash equilibrium searching problem of multi-agent games over weight-unbalanced directed networks. In this problem, the feasible action set of each agent is not only constrained by private convex sets and shared coupling equality, but also constrained by its own uncertain dynamics. Moreover, the local cost function is related to both his own actions and others, and each agent is only allowed access to neighbor information. To address this problem, we propose a neuro-adaptive control strategy based on two-stage design. In the first stage of design process, an approximator with adaptive-gain is designed to generate a virtual trajectory that converges to a necessary Nash equilibrium, the compensator based on consensus-tracking is used to obtain non-neighbor information and several auxiliary state variables are used to exchange information with local neighborhoods on a digraphs to deal with equality constraints. In the second stage of the strategy, we further developed a neuro-adaptive tracking controller based on one-step design for tracking the virtual trajectory. In the controller design, the virtual control variables and the actual control laws are obtained in a collective way, without repeating the design process. We introduce a variable called the minimum learning parameter to reduce the number of online learning parameters of the neural network. By using tools from variational inequality theory and Lyapunov stability theory, we prove that the proposed control strategy can drive all agents reach Nash equilibrium. Finally, the effectiveness of control strategy is verified by simulation example.

A Strategy Formulation Framework for Efficient Screening during the Early Stage of a Pandemic.

For viruses that can be transmitted by contacts of people, efficiently screening infected individuals is beneficial for controlling outbreaks rapidly and avoiding widespread diffusion, especially during the early stage of a pandemic. The process of virus transmission can be described as virus diffusion in complex networks such as trajectory networks. We propose a strategy formulation framework (SFF) for generating various screening strategies to identify influential nodes in networks. We propose two types of metrics to measure the nodes' influence and three types of screening modes. Then, we can obtain six combinations, i.e., six strategies. To verify the efficiencies of the strategies, we build a scenario model based on the multi-agent modelling. In this model, people can move according to their self-decisions, and a virtual trajectory network is generated by their contacts. We found that (1) screening people will have a better performance based on their contact paths if there is no confirmed case yet, and (2) if the first confirmed case has been discovered, it is better to screen people sequentially by their influences. The proposed SFF and strategies can provide support for decision makers, and the proposed scenario model can be applied to simulate and forecast the virus-diffusion process.

Autonomous cooperative formation control of underactuated USVs based on improved MPC in complex ocean environment

A dual model predictive control (DMPC) method based on virtual trajectory is proposed in this paper, in order to achieve autonomous cooperative formation control of underactuated unmanned surface vehicles (USVs) in complex ocean environment. Firstly, the formation tracking error model of the USVs is designed, and considering the large initial state tracking error of USVs, a virtual transition trajectory is put forward to guide the design of tracking controller. To solve the mutation problem caused by misalignment of the follower USV's desired trajectory in the early stage, an improved differential tracker is introduced into virtual leader USV to smooth the desired trajectory of the follower USV. In addition, a dual mode switching strategy is designed to decide when and how to quit the transition of the virtual USV. A nonlinear disturbance observer is introduced to compensate the complex marine environment interference. Finally, by introducing terminal penalty function and linear state feedback controller, the Lyapunov function is constructed to prove the control stability of the proposed model predictive control method in finite time domain, and the effectiveness and reliability of the proposed method are verified by simulation experiments.

RISTrack: Robust Infrared Ship Tracking With Modified Appearance Feature Extraction and Matching Strategy

Infrared (IR) ship tracking is becoming increasingly important in various applications. However, it remains a challenging task as the information that can be obtained from infrared images is limited. Aiming at enhancing IR ship tracking accuracy, we propose an innovative approach by presenting feature integration module (FIM) and backup matching module (BMM). FIM takes appearance feature, complete intersection over union (CIoU), and motion direction metrics into account. Regarding appearance feature extraction, an end-to-end characteristic learning strategy with a cross-guided multi-granularity fusion network is proposed to obtain more integral appearance features and enhance re-identification accuracy, which helps to distinguish individual IR ship targets better. Besides, a backup matching strategy is then used to match the unmatched tracks and detections after cascaded matching. Virtual trajectories are generated for the matched tracks to optimize parameters by parameter optimization module (POM). The accumulation of errors caused by the lack of observations in the Kalman filter is reduced. Thus, the position of IR ships can be estimated more accurately, and more robust IR ship tracking can be achieved. In addition, we present a sequential frame IR ship tracking dataset, providing the first public benchmark for testing IR ship tracking performance. Experimental results indicate that the MOTA, MOTP and IDs of the proposed method are 73.441, 80.826, and 32, respectively, outperforming other state-of-the-art methods. This demonstrates the superior robustness of the proposed method, particularly when the IR ships are occluded or the target texture information is lacking. Our dataset is available at https://github.com/echo-sky/SFIST.

Analysis of socio-economic systems using quantumlike mathematical models based on status functions. Part II

It is proposed to use quantum-like models with the use of status functions for mathematical modeling and subsequent analysis of complex socio-economic systems. The limitations of the methods of classical probability theory and mathematical statistics, as well as the theory of fuzzy sets, algorithms Mamdani, Suzuki and others for solving similar problems are described. A description of the main assumptions that are used in the mathematical modeling of socio-economic objects based on status functions is given. Examples are considered that describe the features of transition paths through intermediate states. An operator for the transition of a socio-economic system to various states, similar to the Hamiltonian, is presented. A spectrum of possible virtual trajectories is introduced to describe transitions to different states. A mathematical model based on status functions is proposed to describe the transition of the system to a measurable state. In the proposed Hamiltonian, the first term represents a subsystem of indica-tors, the second is an analog of the energy of indicators in the information environment. At the same time, terms are distinguished that are analogues of the energies of the system of two controlled indica-tors: interaction, kinetic and potential. The description of the results of mathematical modeling and the analysis of the interaction of two hypothetical indicators of the socio-economic system are given. The indicators are taken from the statistics of innovation indicators of one of the regions of the Russian Federation.

Photometric Visual-Inertial Navigation With Uncertainty-Aware Ensembles

In this article, we propose a visual-inertial navigation system that directly minimizes a photometric error without an explicit data-association. We focus on the photometric error parametrized by pose and structure parameters that is highly nonconvex due to the nonlinearity of image intensity. The key idea is to introduce an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal intensity gradient</i> that accounts for a projective uncertainty of a pixel. Ensembles sampled from the state uncertainty contribute to the proposed gradient and yield a correct update direction even in a bad initialization point. We present two sets of experiments to demonstrate the strengths of our framework. First, a thorough Monte Carlo simulation in a virtual trajectory is designed to reveal robustness to large initial uncertainty. Second, we show that the proposed framework can achieve superior estimation accuracy with efficient computation time over state-of-the-art visual-inertial fusion methods in a real-world UAV flight test, where most scenes are composed of a featureless floor.

Accelerated Jarzynski estimator with deterministic virtual trajectories.

The Jarzynski estimator is a powerful tool that uses nonequilibrium statistical physics to numerically obtain partition functions of probability distributions. The estimator reconstructs partition functions with trajectories of the simulated Langevin dynamics through the Jarzynski equality. However, the original estimator suffers from slow convergence because it depends on rare trajectories of stochastic dynamics. In this paper, we present a method to significantly accelerate the convergence by introducing deterministic virtual trajectories generated in augmented state space under the Hamiltonian dynamics. We theoretically show that our approach achieves second-order acceleration compared to a naive estimator with the Langevin dynamics and zero variance estimation on harmonic potentials. We also present numerical experiments on three multimodal distributions and a practical example in which the proposed method outperforms the conventional method, and we provide theoretical explanations.

A hybrid wave superposition method based on particle filter

In the field of acoustic holography, the wave superposition method (WSM) is one of the classical and widely studied algorithms. To improve the accuracy of sound field reconstruction, a hybrid wave superposition method based on particle filter (PF-HWSM) is proposed. The PF-HWSM mainly includes three steps: (1) calculate the initial source virtual trajectory by 3D-BMUSIC with a rotating microphone array; (2) optimize the virtual trajectory and improve the localization accuracy by the particle filter; (3) configure the equivalent sources according to the optimized localization, and reconstruct the sound field by wave superposition method. Two types of simulations are used to provide a systematic parameter analysis of the traditional WSM-based methods and PF-HWSM, and the comparisons prove the superiority of the proposed method. Finally, two types of experiments in an anechoic room verify that the proposed method achieves high-precision reconstruction. The PF-HWSM provides a method for the equivalent sources configuration of WSM, which can effectively improve the accuracy of sound field reconstruction.