Joint Tracking Research Articles

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

Adaptive angle tracking loop design based on digital phase-locked loop

Angle tracking loop in airborne radar systems is vital in 3D joint tracking loops of range, velocity, and angle. This study concerns two angle tracking issues when an airborne digital array radar system tracks a maneuvering target: slow convergence, and a divergent issue of tracking errors at the final tracking phase. A new angle tracking loop called “constant coefficient angle tracking loop filter” (CCATLF) based on digital phase-locked loop (DPLL) is proposed. To improve tracking performance, a novel algorithm called “adaptive angle tracking loop filter” (AATLF) is also proposed based on CCATLF. In this algorithm, an adaptive equivalent loop noise bandwidth is derived to adjust with the angle by solving an optimization problem that involves angle measuring value and beam pointing. A theoretical error variance formula and stability analysis of the loop are also presented. Experiment results demonstrate that the proposed AATLF algorithm performs better, and is more effective and robust compared with other angle tracking algorithms.

Distributed-Torque-Based Independent Joint Tracking Control of a Redundantly Actuated Parallel Robot With Two Higher Kinematic Pairs

A redundantly actuated parallel robot of the 6RSS mechanism involving two point-contact higher kinematic pairs (HKPs) has been developed for the evaluation of food texture changes during the process of mastication. To accomplish this, a fundamental capability of reproducing complex mandibular motions of human subjects in a biomimetic manner is required. In this paper, first, the mechanism and experimental setup of the robot are described, followed by five performance criteria proposed for the torque distribution across the robot. Second, the distributed torque is employed as a feedforward to enhance the independent joint control for the tracking of the mandibular movement. The frictional effects are compensated for to further improve the tracking accuracy. Finally, experiments are carried out to evaluate and compare the proposed control algorithms with the robot being commanded to reproduce a real human mandibular motion in free chewing, chewing a silicone gel, and chewing a wooden stick. The results illustrate that the robot is able to emulate complex mandibular motions, the distributed-torque-based joint control significantly enhances the motion tracking accuracy, and the friction compensation can further improve the motion tracking performance.

The design and control of a 3DOF lower limb rehabilitation robot

Aiming to assist stroke patients who suffer from motor dysfunction after stroke and reduce the stress of physiotherapists, a 3-degree-of-freedom (3DOF) lower limb rehabilitation robot (LLRR) has been developed for the motion recovery in this paper. At first, a simple and flexible structure of LLRR is designed, which involves hip, knee and ankle joints and can also be adjusted to fit for the different heights of patients. Then, for the controller design, control-oriented model of LLRR is studied and validated by both simulation and experiments, including the kinematic model of mechanical system, friction model of rotation joints and motor model of actuators. Based on the proposed models, an adaptive robust sub-controller is synthesized to make each joint track its designed training trajectory, overcome system uncertainties and reject the disturbances from patient. At last, experiments are carried out to validate the proposed LLRR by the passive movement training.

A survey on joint tracking using expectation–maximization based techniques

Many target tracking problems can actually be cast as joint tracking problems where the underlying target state may only be observed via the relationship with a latent variable. In the presence of uncertainties in both observations and latent variable, which encapsulates the target tracking into a variational problem, the expectation–maximization (EM) method provides an iterative procedure under Bayesian inference framework to estimate the state of target in the process which minimizes the latent variable uncertainty. In this paper, we treat the joint tracking problem using a united framework under the EM method and provide a comprehensive overview of various EM approaches in joint tracking context from their necessity, benefits, and challenging viewpoints. Some examples on the EM application idea are presented. In addition, future research directions and open issues for using EM method in the joint tracking are given.

A Time–Frequency Masking Based Random Finite Set Particle Filtering Method for Multiple Acoustic Source Detection and Tracking

Considering that multiple talkers may appear simultaneously, a time–frequency (TF) masking based random finite set (RFS) particle filtering (PF) method is developed for multiple acoustic source detection and tracking. The time-delay of arrival (TDOA) measurements of multiple sources are extracted by using a time–frequency masking technique, by which each source’s TF bins are clustered and separated in a joint gain-ratio and time-delay histogram. Since a joint detection and tracking problem is considered, both source positions and source numbers are time-varying and need to be estimated. The tracker is built within a RFS Bayesian filtering framework. Essentially, an RFS process is used to characterize the source dynamics that include source appearance/dissappearance and motion trajectories. Latent variables are also introduced to indicate source dynamics and measurement-source associations. Subsequently, a Rao–Blackwellization PF technique is employed so that the source position state can be marginalized and only the latent variables are estimated by using the PF. The main advantage of the proposed approach is that hypothesis-pruning is formulated in a full probabilistic sense. The performance of the proposed approach is demonstrated in real speech recordings as well as in simulated room environments.

Task recognition from joint tracking data in an operational manufacturing cell

This paper investigates the feasibility of using inexpensive, general-purpose automated methods for recognition of worker activity in manufacturing processes. A novel aspect of this study is that it is based on live data collected from an operational manufacturing cell without any guided or scripted work. Activity in a single-worker cell was recorded using the Microsoft Kinect, a commodity-priced sensor that records depth data and includes built-in functions for the detection of human skeletal positions, including the positions of all major joints. Joint position data for two workers on different shifts was used as input to a collection of learning algorithms with the goal of classifying the activities of each worker at each moment in time. Results show that unsupervised and semisupervised algorithms, such as unsupervised hidden Markov models, show little loss of accuracy compared to supervised methods trained with ground truth data. This conclusion is important because it implies that automated activity recognition can be accomplished without the use of ground truth labels, which can only be obtained by time-consuming manual review of videos. The results of this study suggest that intelligent manufacturing can now include detailed process-control measures of human workers with systems that are affordable enough to be installed permanently for continuous data collection.

Joint Detection and Tracking Processing Algorithm for Target Tracking in Multiple Radar System

In this paper, a joint detection and tracking processing (JDTP) algorithm is proposed for target tracking in clutter using a multiple radar system. In this paper, the data association events are formed with a reasonable assumption that each radar can at most receive one measurement originated from a target. Moreover, we explore the idea of feeding the information from the tracker to the detector. In this scenario, the tracker can guide the detectors of multiple radars where to look for a target while keeping the constant false alarm rate property. From a practical point of view, the detection threshold is depressed near where a target is expected to be and elevated where it is unexpected. Simulation results demonstrate the efficiency of the proposed JDTP algorithm, in terms of the detection and the tracking performance, when compared with the existing works.

Joint underwater target detection and tracking with the Bernoulli filter using an acoustic vector sensor

In this paper, we study the problem of joint underwater target detection and tracking using an acoustic vector sensor (AVS). For this challenging problem, first a realistic frequency domain simulation is set up. The outputs of this simulation generate the two dimensional FRequency–AZimuth (FRAZ) image. On this image, the random finite set (RFS) framework is employed to characterize the target state and sensor measurements. We propose to use the Bernoulli filter, which is the optimal Bayes filter emerged from the RFS framework for randomly on/off switching single dynamic systems. Moreover, to increase the performance of detection and azimuth tracking in low signal-to-noise ratio (SNR) scenarios, a track-before-detect (TBD) measurement model for AVS is proposed to be used with the Bernoulli filter. Sequential Monte Carlo (SMC) implementation is preferred for the Bernoulli filter recursions. Extensive simulation results prove the performance gain obtained by the proposed approach both in estimation accuracy and detection range of the system.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Evaluating the microsoft kinect skeleton joint tracking as a tool for home-based physiotherapy

Abstract In physiotherapy, rehabilitation outcome is majorly dependent on the patient to continue exercises at home. To support a continuous and correct execution of exercises composed by the physiotherapist it is important that the patient stays motivated. With the emergence of game consoles such as Nintendo Wii, PlayStation Eye or Microsoft Kinect that employ special controllers or camera based motion recognition as means of user input those technologies have also been found to be interesting for other real-life applications such as providing individual physiotherapy exercises and an encouraging rehabilitation routine. Due to the intended use of those motion tracking systems in a computer-game environment it remains questionable if the accuracy of the skeleton joint tracking hardware and algorithms is suflicient for physiotherapy applications. We present a basic evaluation of the joint tracking accuracy where angles between various body extremities calculated by a Kinect system were compared with a high resolution motion capture system. Results show promising results with tracking deviations between 2.7° and 14.2° with a mean of the absolute deviations of 8.7°.

When and How Is Patella Tracking Best Assessed in Total Knee Arthroplasty Surgery?

Much emphasis has been placed on the role of patella resurfacing in total knee arthroplasty (TKA), yet the impact of soft tissue balancing has frequently been understated. The authors used a novel system to precisely assess patellofemoral joint (PFJ) tracking intraoperatively, to determine the impact of both retinacular reconstruction and tourniquet use on PFJ kinematics. PFJ kinematics assessed intraoperatively for 20 consecutive TKA patients. Measurements were recorded using both the "no thumb technique" and following reconstruction of the retinaculum with two positional sutures. The tourniquet was deflated and both measurements were repeated. Tourniquet inflation was not found to have a significant impact on the patella tracking (mean translation 0.9 mm, p = 0.15). Patella retinacular reconstruction generated a significant medialization of the patella by a mean of 5.5 mm (p < 0.0001) when compared with the traditional retinacular open "no thumb technique." The use of a tourniquet has been shown to have no effect on patella tracking. Reconstruction of the patella retinaculum markedly improves patella tracking, generating a mean medialization of 15%. The authors advocate the routine use of two positional sutures to restore the patella retinaculum, before trialing the patella component, as a reproducible means of assessing the PFJ kinematics.

A Model-Based Joint Detection and Tracking Approach for Multi-Vehicle Tracking With Lidar Sensor

This paper presents a method for joint detection and tracking of vehicles with a scanning laser rangefinder. The lidar measurements of an object have the particularity to be spatially distributed, which generally leads to a detection step before any tracking. Differently, the proposed method relies on the raw measurement processing without any detection step, which improves the overall performance in multiobject tracking while providing good estimation accuracies. The solution uses the sequential Monte Carlo methods by incorporating the geometric invariant of the objects of interest (vehicles). This approach also offers an efficient solution to the problem of multitarget tracking by integrating naturally the track management in the filtering process.

A Joint Multitarget Estimator for the Joint Target Detection and Tracking Filter

Abstract : This paper proposes a joint multitarget (JoM) estimator for the joint target detection and tracking (JoTT) filter. An efficient choice to the unknown JoM estimation constant (i.e., hypervolume around target state estimate) is proposed as a Pareto-optimal solution to a multi-objective nonlinear convex optimization problem. The multi-objective function is formulated as two convex objective functions in conflict. The first objective function is the information theoretic part of the problem and aims for entropy maximization, while the second one arises from the constraint in the definition of the JoM estimator and aims to improve the accuracy of the JoM estimates. The Pareto-optimal solution is obtained using the weighted sum method, where objective weights are determined as linear predictions from autoregressive models. In contrast to the marginal multitarget (MaM) estimator, the target-present decision from the JoM estimator depends on the spatial information as well as the cardinality information in the finite-set statistics (FISST) density. The simulation results demonstrate that the JoM estimator achieves better track management performance in terms of track confirmation latency and track maintenance than the MaM estimator for different values of detection probability. However, the proposed JoM estimator suffers from track termination latency more than the MaM estimator since the localization performance of the JoTT filter does deteriorate gradually after target termination.

Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter.

To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.

RSS-based joint detection and tracking in mixed LOS and NLOS environments

This paper considers the problem of joint detection and tracking in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments by using received signal strength (RSS) measurements. A nonlinear target tracking model with multiple switching parameters has been proposed, in which multiple independent Markov chains are used to describe the switching of target maneuvers and the transition of LOS/NLOS measurements, respectively. Based on the proposed tracking model, a multi-sensor multiple model Bernoulli filter (MMBF) has been developed by employing the random finite set theory which can formulate the joint detection and tracking in a unified framework. To derive a closed-form expression to the MMBF, the Gaussian mixture implementations have been provided by applying the extended Kalman filter technique. A numerical example is provided involving tracking a maneuvering target by a sensor network with 30 nodes. Simulation results confirm the effectiveness of the proposed filter.

A comment on “Portfolio rebalancing in South Africa”

In this comment we add to the discussion on rebalancing portfolios by Sher and Barr (2011). In particular we highlight how a recent rebalancing proposal by Chan and Ramkumar (2011) is found to significantly improve rebalancing performance. An important feature of both studies is that they recognise the significant impact of rebalancing in stressed market conditions and consequently build this into their study designs. In this comment we adapt the tabled results of the first study and portray them in a graphical framework to be comparable with the results of the second study. A concerning feature of Sher and Barr's (2011) results is that the fixed-band strategies they propose still do not seem to adequately control the risks and costs of rebalancing arising in stressed market conditions. We consequently highlight how the Chan and Ramkumar's (2011) rebalancing proposal is especially beneficial in stressed market conditions and is likely to improve on the results of the rebalancing proposal of Sher and Barr (2011). Chan and Ramkumar's (2011) rebalancing proposal is based on a joint cost and tracking error minimisation optimisation. When we take heed of the fact that the South African environment is characterised by higher volatility than developed markets, and more so during stressed periods, this tracking error rebalancing proposal is likely to be practically appealing.

Laser joining with additional material and mechanical joint tracking system

This article presents a tactile system for joint tracking in robotic laser welding with additional material in the form of wire and brazing, using the application example of the Scansonic ALO3 welding head. The construction and control system of a station with disc laser and robot are presented. The article describes the design and the control system of such a welding station constructed using a disc laser and an industrial robot. The study evaluates the influence of the basic parameters of the tracking system during welding and brazing of lap joints with thickness of up to 2 mm.

Bernoulli Filter for Extended Target in Clutter Using Poisson Models

This paper presents a joint detection and tracking filter for a single extended target in the presence of clutter measurements and missed detections. The filter is obtained by adapting the Poisson extended target model into the Bernoulli filter proposed by Mahler. The resulted filter is an optimal extended target joint detection and tracking filter. Predictor and corrector are derived follows the random set filtering framework. A particle filter implementation is presented, in which simplification methods are used to make it easy to be realized. Simulation results show that the proposed filter is effective at detection and tracking of extended target in dense clutter backgrounds.

An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment

This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structuredposture spaceusing the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented asgesture trajectoriesthrough posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.