Tracking Applications Research Articles

МЕТОДИКА КОНТРОЛЬНОГО ТЕСТИРОВАНИЯ ПРИ ИССЛЕДОВАНИИ ВЛИЯНИЯ «СУХОЙ» ИММЕРСИИ НА ПАРАМЕТРЫ ВЫПОЛНЕНИЯ ЗРИТЕЛЬНО-МОТОРНОЙ ЗАДАЧИ

Effects of reduced support, tactile and proprioceptive afferentation on mapping and manual operating the joystick were evaluated with participation of 14 female subjects. The idea was to compare performance of a 15 minute motor-ocular task in the conditions of dry immersion and lying supine on a couch. The subjects were to direct the pointer from the screen center to one of 8 randomly emerging targets. Pointer coordinates and emergence scenarios were registered; time to reach the target and geometry of pointer trajectories were calculated. Comparison was made by 4 characteristics: latent time of reaction, time of implementing the reaction, relative trajectory length, and averaged precision of trajectory to a target and the sum of trajectories. It was found that despite some individual differences, for all participants these 4 characteristics, equally of moving to a specific target or the sum of targets, were essentially similar. The results attest to the applicability of testing supine subject's manual tracking of the pointer to a target as a baseline data collection in dry immersion experiments aimed to define whether support-deprivation impacts effectiveness of manual moving the pointer to target.

Green synthesis of a thermo/photochromic doped cellulose polymer: a biocompatible film for potential application in cold chain visual tracking

A novel photochromic compound has been embedded in a biocompatible cellulose film, creating a composite material that functions as a reusable colorimetric sensor with potential applications in cold chain monitoring.

Exploring Multi-modal Spatial-Temporal Contexts for High-performance RGB-T Tracking.

In RGB-T tracking, there exist rich spatial relationships between the target and backgrounds within multi-modal data as well as sound consistencies of spatial relationships among successive frames, which are crucial for boosting the tracking performance. However, most existing RGB-T trackers overlook such multi-modal spatial relationships and temporal consistencies within RGB-T videos, hindering them from robust tracking and practical applications in complex scenarios. In this paper, we propose a novel Multi-modal Spatial-Temporal Context (MMSTC) network for RGB-T tracking, which employs a Transformer architecture for the construction of reliable multi-modal spatial context information and the effective propagation of temporal context information. Specifically, a Multi-modal Transformer Encoder (MMTE) is designed to achieve the encoding of reliable multi-modal spatial contexts as well as the fusion of multi-modal features. Furthermore, a Quality-aware Transformer Decoder (QATD) is proposed to effectively propagate the tracking cues from historical frames to the current frame, which facilitates the object searching process. Moreover, the proposed MMSTC network can be easily extended to various tracking frameworks. New state-of-the-art results on five prevalent RGB-T tracking benchmarks demonstrate the superiorities of our proposed trackers over existing ones.

Adaptive Distributed Cooperative Tracking Control and Application for Multi-agent Formation Under Communication Constraints

Adaptive Distributed Cooperative Tracking Control and Application for Multi-agent Formation Under Communication Constraints

Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications.

Recent advancements in medical imaging have brought forth various techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, each contributing to improved diagnostic capabilities. Most recently, magnetic particle imaging (MPI) has become a rapidly advancing imaging modality with profound implications for medical diagnostics and therapeutics. By directly detecting the magnetization response of magnetic tracers, MPI surpasses conventional imaging modalities in sensitivity and quantifiability, particularly in stem cell tracking applications. Herein, this comprehensive review explores the fundamental principles, instrumentation, magnetic nanoparticle tracer design, and applications of MPI, offering insights into recent advancements and future directions. Novel tracer designs, such as zinc-doped iron oxide nanoparticles (Zn-IONPs), exhibit enhanced performance, broadening MPI's utility. Spatial encoding strategies, scanning trajectories, and instrumentation innovations are elucidated, illuminating the technical underpinnings of MPI's evolution. Moreover, integrating machine learning and deep learning methods enhances MPI's image processing capabilities, paving the way for more efficient segmentation, quantification, and reconstruction. The potential of superferromagnetic iron oxide nanoparticle chains (SFMIOs) as new MPI tracers further advanced the imaging quality and expanded clinical applications, underscoring the promising future of this emerging imaging modality.

Cognition and behaviour of reception in museum spaces

Abstract In recent years, there has been a growing interest in translation studies within museum settings, enabling museums to enhance visitor engagement and create a more meaningful visiting experience. Within cognitive translation studies, the use of eye tracking technology has emerged as a widely adopted method for investigating translators’ allocation of visual attention and cognitive resources. However, the application of eye tracking in museum translation studies has received limited attention. This study provides a critical review of the utilisation of eye tracking to examine visitors’ perception and reception in museum settings from both behavioural and cognitive perspectives. The article begins by selecting and categorising the reviewed papers. It then describes the methods employed for collecting and analysing eye movement data, followed by summaries and critical comments on the findings from the existing literature. This article further evaluates the reliability, validity, and practicality of existing eye tracking research in museum translation, and finally proposes the potential directions for future cognitive translation studies in museums.

Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking

In order to increase a nonlinear system’s state estimate precision, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) is presented in this study for target tracking. The Gaussian-weighted integral is decomposed into a spherical integral and a radial integral, which are approximated using the spherical simplex-radial rule and second-order Gauss–Laguerre quadrature rule, respectively, and result in the novel simplex cubature rule. To decrease the high-order error terms, cubature points with appropriate weights are taken from the cubature rule and processed using the provided orthogonal matrix. The structure supporting the nonlinear Kalman filter incorporates the altered points and weights and the calculation steps; from this, the updated time and measurement can be inferred. The Gauss–Newton iteration is employed repeatedly to adjust the measurement update until the termination condition is met and the IOSCKF is attained. The proposed algorithms are applied in target tracking, including CV target tracking and spacecraft orbit tracking, and the simulation results reveal that the IOSCKF can achieve higher accuracy compared to the CKF, SCKF, and OSCKF. In spacecraft orbit tracking simulation, compared with the SCKF, the position tracking accuracy and velocity tracking accuracy of the OSCKF are increased by 2.21% and 1.94%, respectively, which indicates that the orthogonal transformation can improve the tracking accuracy. Furthermore, compared with the OSCKF, the position tracking accuracy and velocity tracking accuracy of the IOSCKF are increased by 2.71% and 2.97%, respectively, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper.

Application of visual tracking based on infrared target imaging in aerobics action evaluation system

Application of visual tracking based on infrared target imaging in aerobics action evaluation system

Privacy Considerations of Location Tracking in Social Welfare Applications

COVID-19 rapidly spread worldwide starting in December 2019, reaching its peak during the first quarter of 2021. As of October 28, 2021, COVID-19 deaths have surpassed 5,000,000 globally, with the highest death tolls in the United States, Brazil, and India. Governments scrambled to combat the pandemic using various techniques, including social welfare and pandemic tracking applications. This scramble accelerated the progress of the digital revolution through the proliferation of numerous social welfare applications worldwide. These applications are used for tracking vaccination status, contact tracing, social distancing, symptoms, and positive cases, as well as for enforcing quarantine and lockdown policies and detecting violations. Artificial intelligence and other technology advances raise concerns about security, privacy, and equity since many of these applications work with personal information from one’s health records, employment information, and location data. This paper examines such applications in the Kingdom of Saudi Arabia, the Kingdom of Bahrain, and the United States. Develop best practices that can be followed to enhance the security of and equitable access to social applications.

An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters.

A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.

A comparative study of a traditional localization algorithm and a deep learning model for radioactive particle tracking application

Radioactive particle tracking is a nuclear technique that tracks a sealed radioactive particle inside a volume through a mathematical location algorithm, which is widely applied in many fields such as chemical and civil engineering in hydrodynamics flows. It is possible to reconstruct the trajectory of the radioactive particle using a traditional mathematical algorithm or artificial intelligence methods. In this paper, the traditional algorithm is based on solving a minimization problem between the simulated events and a calibration dataset, and it was written using C++ language. The artificial intelligence method is represented by a deep neural network, in which hyperparameters were defined using a Python optimization library called Optuna. This paper aims to compare the potentiality of both methods to evaluate the accuracy of the radioactive particle tracking technique. This study proposes a simplified model of a concrete mixer, six NaI(Tl) detectors, and a137Cs sealed radioactive particle. The simulated measurement geometry and the dataset (3615 patterns) were developed using the MCNPX code, which is a mathematical code based on the Monte Carlo Method. The results show a mean absolute percentage error (MAPE) of 20.81%, 10.33%, and 16.84% for x, y and z coordinates, respectively, for the traditional algorithm. For the deep neural network, MAPE is 6.87%, 2.70%, and 22.79% respectively for x, y and z coordinates. In addition, an investigation is carried out to analyze whether the size of the calibration dataset influences the performance of both methods.

Arithmetic average density fusion - Part I: Some statistic and information-theoretic results

Finite mixture such as the Gaussian mixture is a flexible and powerful probabilistic modeling tool for representing the multimodal distribution widely involved in many estimation and learning problems. The core of it is representing the target distribution by the arithmetic average (AA) of a finite number of sub-distributions which constitute a mixture. While the mixture has been widely used for single sensor filter design, it is only recent that the AA fusion demonstrates compelling performance for multi-sensor filter design. In this paper, some statistic and information-theoretic results are given on the covariance consistency, mean square error, mode-preservation capacity, and the information divergence of the AA fusion approach. In particular, based on the concept of conservative fusion, the relationship of the AA fusion with the existing conservative fusion approaches such as covariance union and covariance intersection is exposed. A suboptimal weighting approach has been proposed, which jointly with the best mixture-fit property of the AA fusion leads to a max–min optimization problem. Linear Gaussian models are considered for algorithm illustration and simulation comparison, resulting in the first-ever AA fusion-based multi-sensor Kalman filter. This is the first part of a series of papers that provide a comprehensive and thorough study of the AA fusion methodology and its application for target tracking. Extension and derivation of the AA fusion in the context of random finite set filters are provided in the consequent papers.

Multi-Speaker Tracking from Azure Kinect: Performance Evaluation using Generalized Optimal Sub-Pattern Assignment

The software and the hardware features of Azure Kinect show the potential for its application in multiple speaker tracking. We then evaluate the system’s capabilities by conducting tests using our given setup, including various scenarios to evaluate its tracking performance. The tracking performance is calculated using generalized optimal sub-pattern assignment (GOSPA) and multiple objects tracking accuracy (MOTA) metrics. It has been found that the body tracking algorithm can perform well in certain multi-speaker tracking conditions.

Improved SiamCAR with ranking-based pruning and optimization for efficient UAV tracking

UAV tracking is a burgeoning task with vast application prospects in various fields such as agriculture, navigation, and public safety. However, the computational constraints and limited processing speed of drones hinder the deployment of Siamese tracking algorithms. In order to better apply tracking algorithms to drone devices, this paper proposes a novel Ranking-Based SiamCAR (RB-SiamCAR) tracker. The RB-SiamCAR tracker achieves model compression by utilizing a ranking-based filter pruning method, which sorts the filters in the backbone network based on their importance and prunes the filters with low ranks for efficient feature extraction. Additionally, considering existing Siamese trackers overlook the correlation between positive and negative samples and the coherence between classification and localization, we introduce two ranking-based losses. The classification ranking loss ensures that the ranking of positive samples is higher than that of hard negative samples, allowing the tracker to successfully select foreground samples without being fooled by distractors. The IoU-guided ranking loss aims to align the classification with the intersection over union (IoU) of the corresponding localizations of positive samples, enabling well-localized predictions to be represented by high classification confidence. To further enhance the tracking performance, we employ an effective channel attention module (ECA) that allows the network to automatically learn and focus on the most important channels for capturing more discriminative features. Experimental evaluations on UAV tracking benchmarks demonstrate that the proposed RB-SiamCAR outperforms existing state-of-the-art trackers. It is noteworthy that our RB-SiamCAR achieves an impressive tracking speed of nearly 100 fps. The experimental results validate its effectiveness and efficiency in UAV tracking applications.

Leyenes: A gaze-based text entry method using linear smooth pursuit and target speed

Gaze-based writing is one of the most widespread eye tracking applications for human–computer interaction. While eye tracking communication has traditionally been employed as an assistive technology, declining prices of eye trackers now make it a feasible alternative to keyboards or touchscreens in many contexts (for example, the interaction with public info points). In this paper we propose Leyenes, a text entry method based on smooth pursuit, a natural eye movement that occurs when the gaze follows a moving target. Our approach requires no explicit calibration by the user, allowing for more spontaneous interaction and enabling eye input even when calibration is difficult to achieve or maintain. To the best of our knowledge, Leyenes is the first text entry technique based on smooth pursuit that considers both (approximate) gaze speed and position and employs a linear interface instead of the more common circular layouts. The results of the user study we conducted show that the proposed solution is slow but robust, with a very low error rate, which makes it particularly suitable for extemporaneous writing of short text.

Analisis Percepatan Gravitasi Pada Gerak Jatuh Bebas Menggunakan Aplikasi Video Tracker

This study aims to analyze the acceleration of gravity (g) in free fall with a height of 2 meters using a video tracking application. This research is located in the Department of Geological Engineering, Pattimura University, Ambon. The method used in this research is the experimental method. The tools and materials used in this study were tennis balls (2 grams), baseball (80 grams), roll meter, camera and laptop. Based on the results of data analysis, it was found that the value of gravitational acceleration at a height of 2 meters for samples of tennis balls (2 grams) and baseballs (80 grams) was 9.67 ± 0.27 m/s2 and 9.60 ± 0.27 m/s2.

Ultra-low SLL slotted waveguide antenna array using Chebyshev aperture distribution function for MM-wave tracking radar applications

A non-uniform aperture distribution slotted waveguide antenna is presented. The slotted waveguide antenna array is chosen for its ease for aperture distribution control and the flexibility of gain adjustment. Multiple aperture distribution functions, including uniform, binominal, Chebyshev, and Blackman, have been extensively investigated to achieve the best possible side lobe suppression. These distribution functions have been converted to slot displacement from the center line of the antenna. Number of slots in the proposed array is chosen to be eight to satisfy the required gain for mm-wave radar tracking application. The proposed antenna is simulated and optimized using the full wave simulator package (CSTMWS). Rounded edge slots are adapted instead of the sharp ones, as they are more applicable for the CNC fabrication process. An eight-slot waveguide antenna is fabricated and gold-plated, considering the Chebyshev distribution approach. The proposed design achieves very stable radiation characteristics and performance over the operating bandwidth, which extends from 33.6 GHz up to 35.9 GHz. At 35 GHz, the total realized gain of the overall antenna is found to be 13.4 dBi, with an overall radiation efficiency of 95.21 %. An obvious low SLL of −30 dB is obtained at the frequency of 35 GHz. The low side lobe level parameter is very crucial for radar applications as it enhances the overall target SNR and reduces false target observations. The pre-stated antenna characteristics make it a good candidate for mm-wave radar applications and point-to-point communications (P2P).

Multiple object tracking using space-time adaptive correlation tracking

<span>In application of tracking and detecting the suspicious activities, multiple object tracking (MOT) has been given fine attention due to its application as it provides the parallel task of identification and tracking of human. MOT ensures the identification and trajectory for each object frame as they interact, despite the changes in its appearance, occlusion and various other tasks involved. Recent adoption of deep learning has given a new perspective but still achieving high metrics remains a major issue to overcome such issues, this research work presents the integrated architecture of deep convolutional covariance networks (DCCNs) and space-time adaptive correlation tracking (STACT) algorithm with similarity map function (SMF). Moreover, in proposed work, DCCNs is utilized for feature extractions through each frame capturing the distinctive information, STACT is tracking approaches that utilizes the SMF for locating and tracking objects. SMFs are updated for any changes in human appearances and motion, also it deals with occlusion. Here the proposed model is evaluated on MOT17 and MOT20 dataset. Performance analysis is carried out through comparing the existing model and Integrated-DCCN achieves higher metrics.</span>

Attitude of young women aged 18-30 to menstrual tracking apps

Aim. The aim of this article is to explore the attitudes of young women towards menstrual cycle tracking applications.
 Methods. The study involved 85 women aged 18 to 30 years to investigate their experiences and opinions regarding the use of menstrual tracking apps. In this work, the method of diagnostic survey was used.
 Results and conclusion. The vast majority of respondents use apps to track their menstrual cycle. The most frequently mentioned applications are: Flo and Clue. Respondents who do not use these applications cite as the main reason that they did not know about the existence of such applications or do not like the format. The study shows that most respondents learn about these types of applications from the Internet. The vast majority of surveyed women report that the application correctly monitors the menstrual cycle and use it mainly for this purpose.
 Cognitive value. The cognitive value of this article is that women aged 18-30 often use menstrual cycle tracking apps and find them useful but also most of the respondents confirm the credibility of the application in monitoring the menstrual cycle.

Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.