Visual tracking has been widely applied in construction industry and attracted signifi-cant interests recently. Lots of research studies have adopted visual tracking techniques on the surveillance of construction work...Visual tracking has been widely applied in construction industry and attracted signifi-cant interests recently. Lots of research studies have adopted visual tracking techniques on the surveillance of construction workforce, project productivity and construction safety. Until now, visual tracking algorithms have gained promising performance when tracking un-articulated equipment in construction sites. However, state-of-art tracking algorithms have unguaranteed performance in tracking articulated equipment, such as backhoes and excavators. The stretching buckets and booms are the main obstacles of successfully tracking articulated equipment. In order to fill this knowledge gap, the part-based tracking algorithms are introduced in this paper for tracking articulated equipment in construction sites. The part-based tracking is able to track different parts of target equipment while using multiple tracking algorithms at the same sequence. Some existing tracking methods have been chosen according to their outstanding performance in the computer vision community. Then, the part-based algorithms were created on the basis of selected visual tracking methods and tested by real construction sequences. In this way, the tracking performance was evaluated from effectiveness and robustness aspects. Throughout the quantification analysis, the tracking performance of articulated equipment was much more improved by using the part-based tracking algorithms.展开更多
This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper cons...This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet ...Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.展开更多
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
Indoor multi-tracking is more challenging compared with outdoor tasks due to frequent occlusion, view-truncation, severe scale change and pose variation, which may bring considerable unreliability and ambiguity to tar...Indoor multi-tracking is more challenging compared with outdoor tasks due to frequent occlusion, view-truncation, severe scale change and pose variation, which may bring considerable unreliability and ambiguity to target representation and data association. So discriminative and reliable target representation is vital for accurate data association in multi-tracking. Pervious works always combine bunch of features to increase the discriminative power, but this is prone to error accumulation and unnecessary computational cost, which may increase ambiguity on the contrary. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which are settled in various and complex indoor scenes, previous fixed feature selection schemes cannot meet general requirements. To properly handle these problems, first, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features with higher reliability on target representation in the applied scene, and gradually combines features to the minimum requirement of discriminating ambiguous targets; second, a novel depth-invariant part-based appearance model using RGB-D data is proposed which makes the appearance model robust to scale change, partial occlusion and view-truncation. The introduce of RGB-D data increases the diversity of features, which provides more types of features for feature selection in data association and enhances the final multi-tracking performance. We validate our method from several aspects including scene-adaptive feature selection scheme, hierarchical data association scheme and RGB-D based appearance modeling scheme in various indoor scenes, which demonstrates its effectiveness and efficiency on improving multi-tracking performances in various indoor scenes.展开更多
Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable track...Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable tracking,including maintaining continuous target visibility amidst occlusions,ensuring flight safety,and achieving smooth trajectory planning.This paper reviews the latest advancements in UAV-based target tracking,highlighting information prediction,tracking strategies,and swarm cooperation.To address challenges including target visibility and occlusion,real-time prediction and tracking in dynamic environments,flight safety and coordination,resource management and energy efficiency,the paper identifies future research directions aimed at improving the performance,reliability,and scalability of UAV tracking system.展开更多
This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bound...This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bounded gain is proposed by using a new time-varying scaling function.Moreover,a same-side performance function and a novel barrier Lyapunov function are incorporated into the control algorithm,which can compress the feasible domain of tracking error to minimize the overshoot and solve the difficult in tracking error not converging to zero simultaneously.The proposed scheme guarantees the airship capable of operating autonomously with satisfactory transient performance and tracking accuracy,where the performance parameters can be designed artificially and link to the physical process directly.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulation.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths ...To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.展开更多
Index tracking is known to be a passive portfolio management strategy by replicating the performance of a real or virtual index.However,the full replication,which considers all the asserts consisted of the index,often...Index tracking is known to be a passive portfolio management strategy by replicating the performance of a real or virtual index.However,the full replication,which considers all the asserts consisted of the index,often suffers from small and illiquid positions and large transaction costs.Thus,it is preferred to purchase sparse portfolios.Besides,existing literature pointed out the phenomenon of the co-movement in assert returns,indicating that the index tracking problems possibly contain group structures together with sparsity.Based on the consideration of the grouping effects and sparsity in index tracking problems,this paper proposes a grouping sparse index tracking model with nonnegative restrictions.We derive a modified version of coordinate decent algorithm for solving the model.The asymptotic properties are also discussed in detail.To show the efficiency of the model,we apply it into the constrained index tracking problem in Shanghai stock market,i.e.tracking SSE 50 Index.By selecting about 10 stocks,the result shows that nonnegative group lasso outperforms nonnegative lasso in assert allocation.展开更多
Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.展开更多
Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We p...Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.展开更多
Unlike ensemble-averaging measurements,single-molecule tracking provides quantitative information on the kinetics of individual molecules within living cells in real time and may provide insight into the respective mo...Unlike ensemble-averaging measurements,single-molecule tracking provides quantitative information on the kinetics of individual molecules within living cells in real time and may provide insight into the respective molecular interactions behind that.The advancement of single-molecule tracking has been signi-cantly boosted by the development of high-resolution microscopy techniques.In this review,we will discuss this aspect with a particular focus on their recent advance in MINFLUX nanoscopy with feedback approaches where tracking is performed in real time.MINFLUX localization requires fewer than 100 photons from a-1 nm-sized°uorophore,enabling precise tracking.This approach,which demands over an order of magnitude fewer photons than other localization-based techniques(such as STORM,PLAM),allows molecular tracking with single-digit nanometer accuracy in less than 1 ms—an achievement previously unattainable.展开更多
Background:Early identification of concussion-related vision disorders(CRVDs)may improve outcomes by enabling earlier management,referral,and treatment.Objective eye tracking may provide additional data to support the...Background:Early identification of concussion-related vision disorders(CRVDs)may improve outcomes by enabling earlier management,referral,and treatment.Objective eye tracking may provide additional data to support the diagnose of CRVDs.The purpose of this study was to determine the utility of objective infrared eye tracking in identifying CRVDs among adolescents experiencing persisting post-concussive symptoms(PPCS)more than 28 days after injury.Methods:This was a prospective study of adolescents with PPCS evaluated with visio-vestibular examination(VVE),comprehensive vision examination,and an eye tracking device.Results:Of the 108 adolescents enrolled,67(62%)were diagnosed with a CRVD by comprehensive vision examination.On VVE,the near point of convergence break(5.5±3.2 cm vs.3.9±1.7 cm(mean±SD),p<0.001)and recovery(8.1±3.3 cm vs.6.8±2.3 cm,p=0.02)distinguished between those with and without CRVD.Concussion symptom provocation on VVE with horizontal saccades(35(52%)vs.12(29%),p=0.02)and horizontal vestibulo-ocular reflex testing(37(55%)vs.14(34%),p=0.03),and sway on tandem gait under the forward eyes closed condition(25(37%)vs.6(15%),p=0.01)also identified those with CRVD.From the eye tracking device,the BOX score(8.1±5.8 vs.5.2±4.1,p=0.007)and a metric of the left eye tracking along the bottom of the visual target(0.094±0.500 vs.-0.124±0.410,p=0.02)identified those with CRVD,with a multivariable receiver operating characteristic curve analysis,including the BOX score,achieving an area under the receiver operating characteristic curve of 0.7637.Conclusion:CRVDs are common in those with PPCS,with impact on recovery after concussion.Novel eye-tracking metrics can serve as an aid in the identification of those with CRVDs who would benefit from referral for comprehensive diagnosis and treatment.展开更多
The publisher regrets that the Appendix A.Supplementary data was not updated as per author and editor’s request.The publisher would like to apologise for any inconvenience caused.
Multi-axle Swerve-drive Autonomous Mobile Robots(MS-AMRs)equipped with independently steerable wheels are commonly used for high-payload transportation.In this work,we present a novel Model Predictive Control(MPC)meth...Multi-axle Swerve-drive Autonomous Mobile Robots(MS-AMRs)equipped with independently steerable wheels are commonly used for high-payload transportation.In this work,we present a novel Model Predictive Control(MPC)method for MS-AGV trajectory tracking that takes tire wear minimization consideration in the objective function.To speed up the problem-solving process,we propose a hierarchical controller design and simplify the dynamic model by integrating the magic formula tire model and simplified tire wear model.In the experiment,the proposed method can be solved by simulated annealing in real-time on a normal personal computer and by incorporating tire wear into the objective function,tire wear is reduced by 19.19%while maintaining the tracking accuracy in curve-tracking experiments.In the more challenging scene:the desired trajectory is offset by 60 degrees from the vehicle's heading,the reduction in tire wear increased to 65.20%compared to the kinematic model without considering the tire wear optimization.展开更多
Syntax and semantics are two important factors that influence sentence processing.Studies have found different aging effects in syntactic and semantic processing during sentence comprehension.While there is consensus ...Syntax and semantics are two important factors that influence sentence processing.Studies have found different aging effects in syntactic and semantic processing during sentence comprehension.While there is consensus on the aging effects in syntactic processing,the presence of aging in semantic processing remains debated.The present study aimed to explore whether there were aging effects in lexical-semantic information processing in complex sentence.79 participants were recruited to take part in this study,including 40 younger adults(mean age of 21.1±1.19 years)and 39 older adults(mean age of 66.24±3.02 years).Using eye-movement tracking technology and manipulating the animacy of head nouns in Chinese subject relative clauses(SRCs)and object relative clauses(ORCs),we investigated the abilities of young and old adults in relative clauses(RCs)processing.The results of comprehension accuracy revealed a significant effect of aging in RCs processing,with older participants exhibiting poor performance compared with younger counterparts across all four clause conditions.Furthermore,younger participants demonstrated a clear animacy effect in RCs processing,but this effect was not found in older participants.Reading times indicated a prominent aging effect in clause processing,with older participants showing significantly longer reading times across all four types of RCs compared to younger participants.It was observed that processing ORCs in Chinese was relatively easier than processing SRCs.Additionally,a noticeable aging effect in semantic processing was found,specifically,the difficulties of processing SRCs and ORCs vary with the animacy configuration of the head nouns for younger participants but were not observed in older participants.In summary,aging in cognition would also inhinder semantic processing in complex sentence comprehension.展开更多
文摘Visual tracking has been widely applied in construction industry and attracted signifi-cant interests recently. Lots of research studies have adopted visual tracking techniques on the surveillance of construction workforce, project productivity and construction safety. Until now, visual tracking algorithms have gained promising performance when tracking un-articulated equipment in construction sites. However, state-of-art tracking algorithms have unguaranteed performance in tracking articulated equipment, such as backhoes and excavators. The stretching buckets and booms are the main obstacles of successfully tracking articulated equipment. In order to fill this knowledge gap, the part-based tracking algorithms are introduced in this paper for tracking articulated equipment in construction sites. The part-based tracking is able to track different parts of target equipment while using multiple tracking algorithms at the same sequence. Some existing tracking methods have been chosen according to their outstanding performance in the computer vision community. Then, the part-based algorithms were created on the basis of selected visual tracking methods and tested by real construction sequences. In this way, the tracking performance was evaluated from effectiveness and robustness aspects. Throughout the quantification analysis, the tracking performance of articulated equipment was much more improved by using the part-based tracking algorithms.
基金Supported by the Fundamental Research Funds for the Central Universities(2024ZYGXZR047)the National Natural Science Foundation of China(62373156)the Guangdong Basic and Applied Basic Research Foundation(2024A1515011736)。
文摘This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金partially supported by the Natural Science Foundation of Shandong Province,China(No.ZR2023ME009)the National Natural Science Foundation of China(No.51909252)。
文摘Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
基金This work is supported by National Natural Science Foundation of China （NSFC, No. 61340046）, National High Technology Research and Development Program of China （863 Program, No. 2006AA04Z247）, Scientific and Technical Innovation Commission of Shenzhen Municipality （JCYJ20130331144631730, JCYJ20130331144716089）, Specialized Research Fund for the Doctoral Program of Higher Education （No. 20130001110011）.
文摘Indoor multi-tracking is more challenging compared with outdoor tasks due to frequent occlusion, view-truncation, severe scale change and pose variation, which may bring considerable unreliability and ambiguity to target representation and data association. So discriminative and reliable target representation is vital for accurate data association in multi-tracking. Pervious works always combine bunch of features to increase the discriminative power, but this is prone to error accumulation and unnecessary computational cost, which may increase ambiguity on the contrary. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which are settled in various and complex indoor scenes, previous fixed feature selection schemes cannot meet general requirements. To properly handle these problems, first, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features with higher reliability on target representation in the applied scene, and gradually combines features to the minimum requirement of discriminating ambiguous targets; second, a novel depth-invariant part-based appearance model using RGB-D data is proposed which makes the appearance model robust to scale change, partial occlusion and view-truncation. The introduce of RGB-D data increases the diversity of features, which provides more types of features for feature selection in data association and enhances the final multi-tracking performance. We validate our method from several aspects including scene-adaptive feature selection scheme, hierarchical data association scheme and RGB-D based appearance modeling scheme in various indoor scenes, which demonstrates its effectiveness and efficiency on improving multi-tracking performances in various indoor scenes.
基金financial support provided by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045)the Open Research Subject of State Key Laboratory of Intelligent Game(Grant No.ZBKF-24-01)+1 种基金the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240989)the China Postdoctoral Science Foundation(Grant No.2024M754304)。
文摘Unmanned aerial vehicles(UAVs)have become crucial tools in moving target tracking due to their agility and ability to operate in complex,dynamic environments.UAVs must meet several requirements to achieve stable tracking,including maintaining continuous target visibility amidst occlusions,ensuring flight safety,and achieving smooth trajectory planning.This paper reviews the latest advancements in UAV-based target tracking,highlighting information prediction,tracking strategies,and swarm cooperation.To address challenges including target visibility and occlusion,real-time prediction and tracking in dynamic environments,flight safety and coordination,resource management and energy efficiency,the paper identifies future research directions aimed at improving the performance,reliability,and scalability of UAV tracking system.
基金supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.501JCGG2024129003,501JCGG2024129005,501JCGG2024129006),the Fundamental Research Funds for the Central Universities,China(No.YWF-24-JC-09)the National Key Research and Development Program of China(No.2018YFC1506401)。
文摘This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bounded gain is proposed by using a new time-varying scaling function.Moreover,a same-side performance function and a novel barrier Lyapunov function are incorporated into the control algorithm,which can compress the feasible domain of tracking error to minimize the overshoot and solve the difficult in tracking error not converging to zero simultaneously.The proposed scheme guarantees the airship capable of operating autonomously with satisfactory transient performance and tracking accuracy,where the performance parameters can be designed artificially and link to the physical process directly.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulation.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
文摘To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202400514)the Foundation Project of Chongqing Normal University(Grand No.23XLB020)+1 种基金partly supported by Chongqing Social Science Planning Doctoral Program(Grant No.2022BS064)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202301541)。
文摘Index tracking is known to be a passive portfolio management strategy by replicating the performance of a real or virtual index.However,the full replication,which considers all the asserts consisted of the index,often suffers from small and illiquid positions and large transaction costs.Thus,it is preferred to purchase sparse portfolios.Besides,existing literature pointed out the phenomenon of the co-movement in assert returns,indicating that the index tracking problems possibly contain group structures together with sparsity.Based on the consideration of the grouping effects and sparsity in index tracking problems,this paper proposes a grouping sparse index tracking model with nonnegative restrictions.We derive a modified version of coordinate decent algorithm for solving the model.The asymptotic properties are also discussed in detail.To show the efficiency of the model,we apply it into the constrained index tracking problem in Shanghai stock market,i.e.tracking SSE 50 Index.By selecting about 10 stocks,the result shows that nonnegative group lasso outperforms nonnegative lasso in assert allocation.
基金supported by the National Natural Science Foundation of China (No.62202137)the China Postdoctoral Science Foundation (No.2023M730599)the Zhejiang Provincial Natural Science Foundation of China (No.LMS25F020009)。
文摘Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd under Grants RDTC/241149 and RDTC/231095+1 种基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.
基金supported by the Science and Technology Commission of Shanghai Municipality(21DZ1100500)the Shanghai Municipal Science and Technology Major Project+2 种基金the Shanghai Frontiers Science Center Program(2021–2025 No.20)The National Natural Science Foundation of China(32471545)the Natural Science Foundation of Shanghai(24ZR1454300).
文摘Unlike ensemble-averaging measurements,single-molecule tracking provides quantitative information on the kinetics of individual molecules within living cells in real time and may provide insight into the respective molecular interactions behind that.The advancement of single-molecule tracking has been signi-cantly boosted by the development of high-resolution microscopy techniques.In this review,we will discuss this aspect with a particular focus on their recent advance in MINFLUX nanoscopy with feedback approaches where tracking is performed in real time.MINFLUX localization requires fewer than 100 photons from a-1 nm-sized°uorophore,enabling precise tracking.This approach,which demands over an order of magnitude fewer photons than other localization-based techniques(such as STORM,PLAM),allows molecular tracking with single-digit nanometer accuracy in less than 1 ms—an achievement previously unattainable.
基金supported by funding from the National Institution of Neurological Disorders and Stroke(1R41NS103698-01A1 to CLM)。
文摘Background:Early identification of concussion-related vision disorders(CRVDs)may improve outcomes by enabling earlier management,referral,and treatment.Objective eye tracking may provide additional data to support the diagnose of CRVDs.The purpose of this study was to determine the utility of objective infrared eye tracking in identifying CRVDs among adolescents experiencing persisting post-concussive symptoms(PPCS)more than 28 days after injury.Methods:This was a prospective study of adolescents with PPCS evaluated with visio-vestibular examination(VVE),comprehensive vision examination,and an eye tracking device.Results:Of the 108 adolescents enrolled,67(62%)were diagnosed with a CRVD by comprehensive vision examination.On VVE,the near point of convergence break(5.5±3.2 cm vs.3.9±1.7 cm(mean±SD),p<0.001)and recovery(8.1±3.3 cm vs.6.8±2.3 cm,p=0.02)distinguished between those with and without CRVD.Concussion symptom provocation on VVE with horizontal saccades(35(52%)vs.12(29%),p=0.02)and horizontal vestibulo-ocular reflex testing(37(55%)vs.14(34%),p=0.03),and sway on tandem gait under the forward eyes closed condition(25(37%)vs.6(15%),p=0.01)also identified those with CRVD.From the eye tracking device,the BOX score(8.1±5.8 vs.5.2±4.1,p=0.007)and a metric of the left eye tracking along the bottom of the visual target(0.094±0.500 vs.-0.124±0.410,p=0.02)identified those with CRVD,with a multivariable receiver operating characteristic curve analysis,including the BOX score,achieving an area under the receiver operating characteristic curve of 0.7637.Conclusion:CRVDs are common in those with PPCS,with impact on recovery after concussion.Novel eye-tracking metrics can serve as an aid in the identification of those with CRVDs who would benefit from referral for comprehensive diagnosis and treatment.
文摘The publisher regrets that the Appendix A.Supplementary data was not updated as per author and editor’s request.The publisher would like to apologise for any inconvenience caused.
基金supported by the National Research Foundation,Singapore,under its Medium-Sized Center for Advanced Robotics Technology Innovation(CARTIN)。
文摘Multi-axle Swerve-drive Autonomous Mobile Robots(MS-AMRs)equipped with independently steerable wheels are commonly used for high-payload transportation.In this work,we present a novel Model Predictive Control(MPC)method for MS-AGV trajectory tracking that takes tire wear minimization consideration in the objective function.To speed up the problem-solving process,we propose a hierarchical controller design and simplify the dynamic model by integrating the magic formula tire model and simplified tire wear model.In the experiment,the proposed method can be solved by simulated annealing in real-time on a normal personal computer and by incorporating tire wear into the objective function,tire wear is reduced by 19.19%while maintaining the tracking accuracy in curve-tracking experiments.In the more challenging scene:the desired trajectory is offset by 60 degrees from the vehicle's heading,the reduction in tire wear increased to 65.20%compared to the kinematic model without considering the tire wear optimization.
基金supported by the National Social Science Foundation of China(Grant No.24BYY117).
文摘Syntax and semantics are two important factors that influence sentence processing.Studies have found different aging effects in syntactic and semantic processing during sentence comprehension.While there is consensus on the aging effects in syntactic processing,the presence of aging in semantic processing remains debated.The present study aimed to explore whether there were aging effects in lexical-semantic information processing in complex sentence.79 participants were recruited to take part in this study,including 40 younger adults(mean age of 21.1±1.19 years)and 39 older adults(mean age of 66.24±3.02 years).Using eye-movement tracking technology and manipulating the animacy of head nouns in Chinese subject relative clauses(SRCs)and object relative clauses(ORCs),we investigated the abilities of young and old adults in relative clauses(RCs)processing.The results of comprehension accuracy revealed a significant effect of aging in RCs processing,with older participants exhibiting poor performance compared with younger counterparts across all four clause conditions.Furthermore,younger participants demonstrated a clear animacy effect in RCs processing,but this effect was not found in older participants.Reading times indicated a prominent aging effect in clause processing,with older participants showing significantly longer reading times across all four types of RCs compared to younger participants.It was observed that processing ORCs in Chinese was relatively easier than processing SRCs.Additionally,a noticeable aging effect in semantic processing was found,specifically,the difficulties of processing SRCs and ORCs vary with the animacy configuration of the head nouns for younger participants but were not observed in older participants.In summary,aging in cognition would also inhinder semantic processing in complex sentence comprehension.
