Joint Tracking Research Articles

High-Speed Dynamic Projection Mapping onto Human Arm with Realistic Skin Deformation

Dynamic projection mapping for a moving object according to its position and shape is fundamental for augmented reality to resemble changes on a target surface. For instance, augmenting the human arm surface via dynamic projection mapping can enhance applications in fashion, user interfaces, prototyping, education, medical assistance, and other fields. For such applications, however, conventional methods neglect skin deformation and have a high latency between motion and projection, causing noticeable misalignment between the target arm surface and projected images. These problems degrade the user experience and limit the development of more applications. We propose a system for high-speed dynamic projection mapping onto a rapidly moving human arm with realistic skin deformation. With the developed system, the user does not perceive any misalignment between the arm surface and projected images. First, we combine a state-of-the-art parametric deformable surface model with efficient regression-based accuracy compensation to represent skin deformation. Through compensation, we modify the texture coordinates to achieve fast and accurate image generation for projection mapping based on joint tracking. Second, we develop a high-speed system that provides a latency between motion and projection below 10 ms, which is generally imperceptible by human vision. Compared with conventional methods, the proposed system provides more realistic experiences and increases the applicability of dynamic projection mapping.

Implementation of a Character Recognition System Based on Finger-Joint Tracking Using a Depth Camera

The joint tracking-based writing system refers to writing characters by changing the position of the finger joint. It is a new research field in interaction-based input systems. However, joint tracking is a very challenging task. In this article, we present a new method for a finger-joint tracking-based character recognition system using a 3-D camera. The proposed method tracks the finger-joint from 3-D information to identify a numerical digit, alphabet, character, special key, or symbol using the distance between the thumb tip and another finger-joint. The recognition is based on Euclidean distance thresholding and geometric slope techniques. Joint data are stored in a 3-D matrix to assign the 3-D coordinate values. The exact character is identified according to the specified definitions. First, a single hand-based digit recognition method is introduced, in which the left or right hand is used. Second, a double hand-based writing system is presented in which both hands are used simultaneously; this system features a full keyboard with 124 different characters. Our results show an overall accuracy of 91.95% and 91.85% for single-hand and double-hand recognition, respectively; with a recognition time of less than 60 ms for each character. An important contribution of this article is that the proposed system can work in both light and dark environments, requires only a small computation area and has a large number of character sets (124 characters). In addition, a region-based user study has been conducted to verify the approach.

RETRACTED ARTICLE: Smart wearable medical devices for Isometric Contraction of muscles and joint tracking with gyro sensors for elderly people

People with a healthy lifestyle modulate cycles of stimulation through posture and social factors. In general, muscle activation is generally controlled by exoskeleton patterns, mainly personal muscle control during voluntary movement. Therefore, a survey suggested that Persons with nervous system disorders and older people cannot modulate their muscle activity rhythm and have issues with movement control. Hence, this study concentrates on Linear Quadratic Estimation Hybridised Computer Algorithm (LQE-HC) to detect the muscles’ behaviour method using accelerometers to find joints’ angular speed. Remote monitoring can give valuable data on the regular exercise level and functional potential of individuals. Furthermore, a low drift in the observed speed leads to a longer integration error. Supplementary data from gyroscopes are normally fused with the Linear Quadratic Estimation (LQE). A computer algorithm structurally measures instructions to a wearable device to conduct an optimal muscle stimulation sequence inducted for goal muscle powers. This article introduces a basic principle, conceptual description, and method for the human muscular force regulation and its adaptation to a motor system with upper extremity exoskeleton-type wearable devices.

Joint tracking coefficients and the time delay of a nonstationary linear system, preceded by a static nonlinearity

SummaryThe paper considers a block‐oriented nonlinear Hammerstein system that consists of a linear block with real‐valued time‐varying unknown coefficients and a pure time delay, preceded by the static nonlinearity. The joint tracking of linear block coefficients and the time delay while processing observation samples is under consideration. The difficulty of the tracking process consists in the initial nonconvexity of the objective function to be minimized, in respect of the time delay. To obtain current estimates of the true coefficients and the time delay it is necessary to overcome the multiextremality of the objective function. A global optimization problem could be solved by changing a form of the nonconvex objective function into a convex one. The optimization problem has been subdivided into two problems that are analyzed in this article, as well. The first one consists of removal of the objective function multiextremality in respect of the time delay, while hunting for the global extremum. Therefore, the well‐known approach based on the method of corrective operators, which is used to transform the multiextremal criterion into a unimodal function for the nonstationary Wiener system, is developed here for the Hammerstein one. The other problem consists of the applicability of unified adaptive algorithms, used for Wiener systems in the case of Hammerstein systems. The recursive parametric identification technique, based on the adaptive algorithm procedures, is developed here for the nonstationary block‐oriented Hammerstein systems, too. The applicability of algorithms is supported by various simulation tests solved by computer for distinct inputs and for two static nonlinearities.

Thermal challenges of replacing jointed rails with CWR on steel railway bridges

Visual inspection of a steel truss railway bridge revealed multiple fatigue cracks on the top member of the truss, mainly attributed to the high impact loads of crossing wheels over the jointed tracks on the bridge. The proposal to replace the jointed track with continuously welded rails (CWR) raised a number of issues regarding the thermal interaction of track and bridge, as the bridge experiences extreme temperature variations. Incorporation of high longitudinal restraint between the track and the bridge may increase the risk of track buckling failure at bridge transition zones, since bridge thermal expansion during hot weather can add compressive forces to the rails transferred through the track fastening system. Reducing longitudinal restraining may increase the size of a rail gap resulting from cold weather rail fracture. An optimal longitudinal fastening profile between the bridge and the track therefore is required to mitigate thermal issues both in hot summers and cold winters. To do so, a numerical model of the track and the bridge is developed in Abaqus and calibrated based on the results of tests performed on the bridge and available literature. Three longitudinal fastening profiles between the bridge and the track are introduced in the numerical model and performance of each for mitigating buckling failure and cold weather rail fracture are analyzed. It is concluded that providing 50% resilient fasteners and 50% zero toe load fasteners on each span can be a sound approach to mitigating the thermal issues of CWR tracks on the bridge.

Sensor Management for Search and Track Using the Poisson Multi-Bernoulli Mixture Filter

A sensor management method for joint multitarget search and track problems is proposed, where a single user-defined parameter allows for a tradeoff between the two objectives. The multitarget density is propagated using the Poisson multi-Bernoulli mixture filter, which eliminates the need for a separate handling of undiscovered targets and provides the theoretical foundation for a unified search and track method. Monte Carlo simulations of two scenarios are used to evaluate the performance of the proposed method.

Wearable Medical Gyro Sensors Based on Joint Tracking for Elderly

This article concentrates on Linear Quadratic Estimation Hybridised Computer Algorithm (LQE-HC) which has been proposed to detect the behavior method of muscles with the implementation of accelerometers to find the angular speed of joints. Remote monitoring can give valuable data on regular exercise level and functional potential of individuals. Furthermore, a low drift in the observed speed leads to a longer integration error. Supplementary data from gyroscopes are normally fused with the Linear Quadratic Estimation (LQE). A computer algorithm structurally measures instructions to a wearable device so that an optimal muscle stimulation sequence is inducted for goal muscle powers.

Multiple extended stealth target tracking from image observation based on non‐linear sub‐random matrices approach

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi-Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB-filter-based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM-MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track-before-detect (TBD) approach, which is an efficient way to track low-observable ESTs, is proposed. In EST-RMM-TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST-sub-RMM-TBD composed of sub-ellipses is introduced, each representing an RMM. Based on such models, a sub-RMM-MB-TBD filter is used to estimate the kinematic states and extensions of sub-objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non-linear kinematic EST state is applied. The results indicate that the proposed SMC-sub-RMM-MB-TBD filter has more accurate cardinality estimation and smaller optimal sub-pattern assignment errors than the recent extended tracking filters.

Joint tracking of multiple quantiles through conditional quantiles

The estimation of quantiles is one of the most fundamental data mining tasks. As most real-time data streams vary dynamically over time, there is a quest for adaptive quantile estimators. The most well-known type of adaptive quantile estimators is the incremental one which documents the state-of-the art performance in tracking quantiles. However, the absolute vast majority of incremental quantile estimators fail to jointly estimate multiple quantiles in a consistent manner without violating the monotone property of quantiles. In this paper, first we introduce the concept of conditional quantiles that can be used to extend incremental estimators to jointly track multiple quantiles. Second, we resort to the concept of conditional quantiles to propose two new estimators. Extensive experimental results, based on both synthetic and real-life data, show that the proposed estimators clearly outperform legacy state-of-the-art joint quantile tracking algorithms in terms of accuracy while achieving faster adaptivity in the face of dynamically varying data streams.

Performance Evaluation of Joint Tracking and Classification

Joint tracking and classification (JTC) is gaining momentum in recent years. Many algorithms have been proposed. However, not enough attention has been paid to JTC evaluation, although it is important in practice. In this paper, we deal with evaluating the goodness and credibility of JTC. For the JTC goodness, tracking and classification so far have been largely evaluated separately without considering the interdependence of tracking and classification. We propose a joint measure-joint probability divergence (JPD)-to quantify tracking error, misclassification and their interdependence. The basic idea of JPD is to measure the closeness between the cumulative distribution functions of the perfect JTC and the JTC to be evaluated. The proposed method has a number of attractive properties. Some results from algorithms can be regarded as self-assessments. The credibility problem is concerned with whether these assessments are credible or how credible they are. We define the credibility problem for decision and propose an associated noncredibility index (NCI). We also propose a joint NCI (JNCI) to quantify the noncredibility of estimation and decision jointly. Four examples are presented to demonstrate how well the JPD and JNCI reflect the joint performance of tracking and classification.

A composite controller for manipulator with flexible joint and link under uncertainties and disturbances

In this article, a composite controller is proposed for the manipulator with the flexible joint and link under uncertainties and time-varying disturbances. The dynamic of the system is developed by the Euler–Lagrange and assumed mode method, which is a nonlinear, strong coupling, and underacted system. Therefore, based on the singular perturbation theory, the dynamic is decomposed into a slow and fast subsystem. For the slow dynamic, a novel adaptive-gain super-twisting sliding mode controller is designed to guarantee joint tracking under the uncertainties and disturbances. For the fast dynamics, adaptive dynamic programming is used to deal with the uncertainty. The simulation result shows that the proposed composite controller can effectively track the trajectory and suppress the vibration simultaneously.

Joint tracking and classification of multiple extended targets via the PHD filter and star-convex RHM

For joint tracking and classification (JTC) of multiple extended targets in the presence of clutter and detection uncertainty, this paper proposes a recursive algorithm based on the probability hypothesis density (PHD) filter and star-convex random hypersurface model (RHM), resulting in the JTC-RHM-PHD filter. By modeling the extent state via the star-convex RHM, the JTC-RHM-PHD filter can classify the extended targets using the feature information of size and shape, instead of the size information only in the random matrix model (RMM)-based JTC method. To integrate the prior class-dependent information into the tracking procedure and obtain the recursive expressions of the JTC-RHM-PHD filter, the relationship between the instantaneous extent state and prior class-dependent information is first constructed. Then, the target state is remodeled by two vectors: one for the kinematic state and another for the extent state. The kinematic state is estimated via a Kalman-like filter. For the extent state, the measurement-based extent state and class-based extent state are obtained via the sensor measurements and prior class-dependent information, respectively. The two kinds of extent states are used to determine the target class. At last, a Gamma-Gaussian-Gaussian mixture implementation of the JTC-RHM-PHD filter is presented. Simulation results indicate that: (1) Compared with the RMM-based JTC method, the proposed JTC-RHM-PHD filter can correctly classify multiple extended targets with similar sizes. (2) Compared with the RHM-PHD filter, the proposed JTC-RHM-PHD filter has superior tracking performance with a similar algorithm complexity.

Joint Multitarget Detection and Tracking in Multipath Environment Using Expectation Maximization Algorithm

Joint detection and tracking is fundamentally important in signal processing, navigation, and radar applications. Especially, multitarget detection and tracking in multipath environments is a promising issue and broadening range for joint detection and tracking. However, its wide adoption in real-world systems is challenged by the unknown data association, complex and changeable motion environment, and data mutual coupling. To address those problems, we propose a joint detection and tracking scheme based on expectation maximization (EM) algorithm. By alternately computing the complete likelihood function and optimizing it with respect to target existence state (detection) and target kinematic state (tracking), the proposed scheme iteratively optimizes estimation and data association. Furthermore, we provide a hybrid forward and backward algorithm during the M-step to deal with the coupling of the target existence state and target kinematic state. We also provide a convergence speed-up algorithm based on K-means method and stochastic initialization method to accelerate convergence speed. Finally, we verify the effectiveness of the proposed joint detection and tracking scheme by conducting extensive simulations with different tracking scenarios. The simulation results show that the proposed algorithm not only improves the detection and tracking performance, but also stays stable performance in complex and changeable motion environment and high noise background.

KM Learning for Millimeter-Wave Beam Alignment and Tracking: Predictability and Interpretability

A data representation technique dubbed Kolmogorov model (KM), has been applied to the beam alignment problem in large-dimensional antenna systems. The previous learning-based beam alignment solely focused on utilizing the predictive power of KM, i.e., the capability of predicting the outcome of random variables that are outside the training set, to reduce the beam training overhead. However, a distinctive feature of KM, namely, the interpretability which enables the capability of extracting additional information hidden inside the data, has not yet been exploited. Moreover, the prohibitively high computational complexity of the existing KM learning algorithm offsets the benefits brought by KM and hampers its application to large-scale problems. In this paper, we propose a joint beam alignment and tracking framework by incorporating the predictability and interpretability of KM. Especially, our proposed scheme enables a novel interpretable beam tracking that reveals insights on relations among the sounded observations to alleviate the beam sounding overhead after the initial beam alignment. To reduce the computational complexity of KM learning, two enhancement approaches, based on discrete monotonic optimization (DMO) and dual optimization, respectively, are proffered. Numerical results demonstrate that the proposed methods can reduce the computational cost of the existing KM learning algorithm by up to three orders of magnitude. Furthermore, the proposed methods show superior beam alignment and tracking performance over other state-of-the-art techniques, notably in the low signal-to-noise ratio (SNR) regime.

Elderly Care Based on Hand Gestures Using Kinect Sensor

Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.

Joint detection and tracking of non‐ellipsoidal extended targets based on cubature Kalman‐CBMeMBer sub‐random matrices filter

Joint detection and tracking of multiple extended targets (ETs) from image observations is a challenging radar technology; especially for extended stealth targets (ESTs). This work provides a new approach for the ESTs tracking under the non-linear Gaussian system based on track-before-detect (TBD) approach. The sequential Monte Carlo cardinality-balanced multi-target multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with TBD approach. However, this filter suffers from the particles’ degradation problem seriously; especially for ETs tracking. Recently, the cubature Kalman (CK)-CBMeMBer filter which employs a third-degree spherical-radical cubature rule has been proposed to handle the non-linear models, the CK-CBMeMBer filter is more accurate and more principled in mathematical terms compared to SMC-CBMeMBer filter. To this point, the authors address a TBD of ESTs with extended CK-CBMeMBer filter based on random matrix model (RMM), which is an efficient way to track ellipsoidal ESTs. In RMM-ESTs scenarios, although the extension ellipsoid is efficient, it may not be accurate enough because of lacking useful information, such as size, shape, and orientation. Therefore, they introduce a filter composed of sub-ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed filter.

Monitoring of subsynchronous oscillation in a series‐compensated wind power system using an adaptive extended Kalman filter

The characteristics of subsynchronous oscillation (SSO) in series-compensated wind power systems are significantly affected by the system's operating condition. Besides the variation in the magnitude and frequency of the SSO during an SSO event, the fundamental frequency may also deviate from its nominal value. This study aims at capturing the dynamics of both subsynchronous and fundamental frequency components simultaneously. This work first explores the strengths and weaknesses of various Kalman filtering based frequency tracking algorithms for joint tracking of the fundamental and SSO components. Then, it proposes a novel adaptive extended Kalman filtering (AEKF) algorithm, in which the process noise covariance is updated online by maximising the probability density function of the predicted error. The process noise covariance factor changes to positive non-zero value whenever the frequency of the fundamental component deviates. Thus, the proposed AEKF extracts the time-varying subsynchronous component while also tracking the small variations in the fundamental frequency. The tracking performance of the AEKF is validated on computer-generated test signals as well as on the electromagnetic transient simulation model of an actual wind power system facing SSO. The captured fundamental and subsynchronous dynamics can be used for designing monitoring, protection, and control schemes for the SSO.

Joint detection and tracking of extended stealth targets from image observations based on subrandom matrices

The challenge of joint detection and tracking of multiple extended targets (ETs) arises in many radar applications, especially for extended stealth targets (ESTs) with small signal-to-noise ratio (SNR). Recently, the multi-Bernoulli (MB) filter-based random matrix model (RMM) has been proposed for tracking ellipsoidal ETs. The MB-RMM filter depends on the measurements (position and range rate), which are extracted by thresholding the received signal. It is acceptable if SNR is high. The threshold must be less than the sufficient probability to avoid a high rate of false detections. This assumption is unsuitable for tracking extended-stealth targets (ESTs) with unknown detection probability. To this point, we address a track-before-detect-MB-RMM approach, which is an efficient way to solve the unknown detection profile issue. There is a considerable advantage of using unthresholded data in simultaneous detection and tracking. In this scenario, although the extension ellipsoid is efficient, it may not be accurate enough because of a lack of useful information, such as size, shape, and orientation. Therefore, we introduce a filter composed of subellipses where each one is represented by an RMM. Furthermore, a sequential Monte Carlo implementation is applied to estimate nonlinear kinematic ESTs’ state. The results confirm the effectiveness and robustness of the proposed filter.

Market-Oriented Consumption Model Based on the Joint Tracking of Renewable Energy Generation Curve of “Shared Energy Storage & Demand Side Resources”

The fluctuation of renewable energy output brings many challenges to the operation of power system, which also indirectly affects the consumption of renewable energy. This paper proposes a market-oriented consumption model based on “shared energy storage and demand side resources” to track renewable energy generation curve. This paper combs the benefits of each party when participating in the transaction, and gives an example to illustrate how the parties benefit from the transaction. The market transaction mode proposed in this paper can enhance the user’s adaptability to renewable energy output by using energy storage equipment; additionally, it can avoid the risk of building energy storage facilities due to participation in the transaction. This provides a feasible idea for further improving the capacity of renewable energy consumption in China.

A new hybrid force/position control approach for time-varying constrained reconfigurable manipulators

In this manuscript, a new hybrid force/position control approach has been proposed for time-varying constrained reconfigurable manipulators. In order to design the controller, firstly a reduced-order dynamic model of time-varying constrained manipulator system is presented. The uncertainties in the dynamical model of the system are inevitable; therefore the model-based control approach is inadequate to handle these systems. Therefore, inspired by this consideration, whatsoever partial information is available about the dynamics of the system, have been used for controller design purpose. The model-dependent control scheme is integrated with the neural network-based model-free control scheme. Radial basis function neural network is used for the estimation of the unknown dynamics of the system. Next, to overcome the aftereffects of the friction terms and neural network reconstruction error, an adaptive compensator is added to the part of the controller. For the stability analysis of the presented control scheme, the Lyapunov theorem and Barbalat’s lemma are utilized. The designed control scheme guarantees that tracking errors of the joints and the force tracking error remain inside the desired levels and the joint tracking errors converge to zero asymptotically. Finally, comparative computer simulations show the superiority and the applicability of the developed control method applied over a 2-DOF time-varying constrained reconfigurable manipulator.