3D Target Tracking Research Articles

A digital approach to dynamic jaw tracking using a target tracking system and a structured-light three-dimensional scanner

PurposeThis technical procedure describes a method for tracking mandibular movement using a three-dimensional (3D) optical scanner and target tracking system to digitally portray the motion of the mandible and temporomandibular joints by merging cone beam computed tomography (CBCT) data. MethodsFour nonreflective targets were attached to the labial surface of the incisors in a noncolinear arrangement. Mandibular movement was tracked directly using a 3D facial scanner and target tracking software after merging facial scanning data, digital data obtained from a diagnostic cast, and CBCT scan data based on several landmarks of the anterior teeth. The moving path of the subjects’ mandible was converted to CBCT-based data to confirm the actual movement of the mandible and temporomandibular joints. ConclusionsThe digital implementation of mandibular movement using a 3D optical scanner and target tracking system is not prone to the same restrictions and limitations inherent in mechanical equipment; therefore, it is possible to reconstruct more realistic movement(s). This technique can be used in a wide variety of dental applications involving movement of the mandibular jaw, such as fabrication of dental prostheses, or for the diagnosis and treatment of temporomandibular joint disease.

Adaptive tracking algorithm based on 3D variable turn model

Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the problem of 3D target tracking with strong maneuverability, on the basis of the modified three-dimensional variable turn (3DVT) model, an adaptive tracking algorithm is proposed by combining with the cubature Kalman filter (CKF) in this paper. Through ideology of real-time identification, the parameters of the model are changed to adjust the state transition matrix and the state noise covariance matrix. Therefore, states of the target are matched in real-time to achieve the purpose of adaptive tracking. Finally, four simulations are analyzed in different settings by the Monte Carlo method. All results show that the proposed algorithm can update parameters of the model and identify motion characteristics in real-time when targets tracking also has a better tracking accuracy.

3D AOA target tracking using distributed sensors with multi-hop information sharing

This paper investigates the problem of angle-of-arrival (AOA) target tracking in the three-dimensional (3D) space using multiple distributed sensors. Some sensors cannot share information directly because of the communication distance constraint arising from the broadcast power limitation of communication equipment. This can lead to reduced estimation performance as a result of limited information exchange. To improve the estimation performance, delayed information transmitted by intermediate sensors through multiple hops is utilized. A neighborhood matrix is proposed to sort the received information in terms of the number of hops. The information is classified into current information, 1-step delayed information and multi-step delayed information. A distributed pseudolinear Kalman filter (DPLKF) is developed based on out-of-sequence measurement updates to process the current and delayed information. Simulation examples demonstrate the effectiveness of the proposed algorithm in improving the tracking performance, in particular, during the initial convergence phase.

TU‐AB‐BRA‐09: A Novel Method of Generating Ultrafast Volumetric Cine MRI (VC‐MRI) Using Prior 4D‐MRI and On‐Board Phase‐Skipped Encoding Acquisition for Radiotherapy Target Localization

Purpose:To develop a technique generating ultrafast on‐board VC‐MRI using prior 4D‐MRI and on‐board phase‐skipped encoding k‐space acquisition for real‐time 3D target tracking of liver and lung radiotherapy.Methods:The end‐of‐expiration (EOE) volume in 4D‐MRI acquired during the simulation was selected as the prior volume. 3 major respiratory deformation patterns were extracted through the principal component analysis of the deformation field maps (DFMs) generated between EOE and all other phases. The on‐board VC‐MRI at each instant was considered as a deformation of the prior volume, and the deformation was modeled as a linear combination of the extracted 3 major deformation patterns. To solve the weighting coefficients of the 3 major patterns, a 2D slice was extracted from VC‐MRI volume to match with the 2D on‐board sampling data, which was generated by 8‐fold phase skipped‐encoding k‐space acquisition (i.e., sample 1 phase‐encoding line out of every 8 lines) to achieve an ultrafast 16–24 volumes/s frame rate. The method was evaluated using XCAT digital phantom to simulate lung cancer patients. The 3D volume of end‐ofinhalation (EOI) phase at the treatment day was used as ground‐truth onboard VC‐MRI with simulated changes in 1) breathing amplitude and 2) breathing amplitude/phase change from the simulation day. A liver cancer patient case was evaluated for in‐vivo feasibility demonstration.Results:The comparison between ground truth and estimated on‐board VC‐MRI shows good agreements. In XCAT study with changed breathing amplitude, the volume‐percent‐difference(VPD) between ground‐truth and estimated tumor volumes at EOI was 6.28% and the Center‐of‐Mass‐Shift(COMS) was 0.82mm; with changed breathing amplitude and phase, the VPD was 8.50% and the COMS was 0.54mm. The study of liver patient case also demonstrated a promising in vivo feasibility of the proposed methodConclusion:Preliminary results suggest the feasibility to estimate ultrafast VC‐MRI for on‐board target localization with phase skipped‐encoding k‐space acquisition.Research grant from NIH R01‐184173

Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Only target kinematic information is used in most conventional tracking systems, such as a radar or a sonar. Target attitude angles, which provide information about future trajectory curvature before radar measurement, can be used to improve tracking accuracy. The aim of this study is to track three-dimensional (3D) target with attitude angles (yaw and pitch) and radar measurement. Target velocity variations in each coordinate under attitude angles are derived after motion analysis under yaw and pitch angles separately. Then tracking models, the state vector of which includes attitude angles, are presented for target tracking. Targeting at non-zero mean characteristics of attitude measurement, and based on analysing Gaussian sum filter (GSF) and cubature Kalman filter (CKF), a Gaussian sum CKF (GSCKF) is presented to improve the filtering ability of non-linear non-Gaussian systems. Meanwhile, tracking models with different attitude components are established according to the changing law during target motion, and manoeuvering attitude angles are estimated through model switch. A comparison of performance with and without the use of attitude angles shows the benefits of attitude angle-aided 3D target tracking. Simulation results of GSFs with different sub-filters demonstrate that the performance of the presented GSCKF has improved over conventional GSFs.

Multiple 3D Target Tracking in Binary Wireless Sensor Network

In this paper, 3D target tracking in wireless sensor network (WSN) is considered. Unlike other published work, this paper addresses the problem of estimating locations of different targets in 3D environment. Each sensor makes T observations to get local decisions. These decisions are modulated using on-off keying (OOK) and sent to a central node called fusion center. The fusion center detects all decisions sent from all sensors to process them in order to estimate the targets locations using maximum likelihood estimator. This approach generalizes the existing work in the literature about a single target localization if we considered that the number of targets equal one. The corresponding Cramer-Rao lower bound (CRLB) has been also derived. Furthermore, simulation results are provided and discussed. 

Evaluation of Pediatric ATD Biofidelity as Compared to Child Volunteers in Low-Speed Far-Side Oblique and Lateral Impacts

Objective: Motor vehicle crashes are a leading cause of injury and mortality for children. Mitigation of these injuries requires biofidelic anthropomorphic test devices (ATDs) to design and evaluate automotive safety systems. Effective countermeasures exist for frontal and near-side impacts but are limited for far-side impacts. Consequently, far-side impacts represent increased injury and mortality rates compared to frontal impacts. Thus, the objective of this study was to evaluate the biofidelity of the Hybrid III and Q-series pediatric ATDs in low-speed far-side impacts, with and without shoulder belt pretightening.Methods: Low-speed (2 g) far-side oblique (60°) and lateral (90°) sled tests were conducted using the Hybrid III and Q-series 6- and 10-year-old ATDs. ATDs were restrained by a lap and shoulder belt equipped with a precrash belt pretightener. Photoreflective targets were attached to the head, spine, shoulders, and sternum. ATDs were exposed to 8 low-speed sled tests: 2 oblique nontightened, 2 oblique pretightened, 2 lateral nontightened, 2 lateral pretightened. ATDs were compared with previously collected 9- to 11-year-old (n = 10) volunteer data and newly collected 6- to 8-year-old volunteer data (n = 7) tested with similar methods. Kinematic data were collected from a 3D target tracking system. Metrics of comparison included excursion, seat belt and seat pan reaction loads, belt-to-torso angle, and shoulder belt slip-out.Results: The ATDs exhibited increased lateral excursion of the head top, C4, and T1 as well as increased downward excursion of the head top compared to the volunteers. Volunteers exhibited greater forward excursion than the ATDs in oblique nontightened impacts. These kinematics correspond to increased shoulder belt slip-out for the ATDs in oblique tests (ATDs = 90%; volunteers = 36%). Contrarily, similar shoulder belt slip-out was observed between ATDs and volunteers in lateral impacts (ATDs = 80%; volunteers = 78%). In pretightened impacts, the ATDs exhibited reduced lateral excursion and torso roll-out angle compared to the volunteers.Conclusions: In general, the ATDs overestimated lateral excursion in both impact directions, while underestimating forward excursion of the head and neck in oblique impacts compared to the pediatric volunteers. This was primarily due to pendulum-like lateral bending of the entire ATD torso compared to translation of the thorax relative to the abdomen prior to the lateral bending of the upper torso in the volunteers, likely due to the multisegmented spinal column in the volunteers. Additionally, the effect of belt pretightening on occupant kinematics was greater for the ATDs than the volunteers.

A Power Allocation Approach for 3D Target Tracking in Multistatic Radar Systems

A Power Allocation Approach for 3D Target Tracking in Multistatic Radar Systems

Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

With the evolution and fusion of wireless sensor network and embedded camera technologies, distributed smart camera networks have emerged as a new class of systems for wide-area surveillance applications. Wireless networks, however, introduce a number of constraints to the system that need to be considered, notably the communication bandwidth constraints. Existing approaches for target tracking using a camera network typically utilize target handover mechanisms between cameras, or combine results from 2D trackers in each camera into 3D target estimation. Such approaches suffer from scale selection, target rotation, and occlusion, drawbacks typically associated with 2D tracking. In this paper, we present an approach for tracking multiple targets directly in 3D space using a network of smart cameras. The approach employs multi-view histograms to characterize targets in 3D space using color and texture as the visual features. The visual features from each camera along with the target models are used in a probabilistic tracker to estimate the target state. We introduce four variations of our base tracker that incur different computational and communication costs on each node and result in different tracking accuracy. We demonstrate the effectiveness of our proposed trackers by comparing their performance to a 3D tracker that fuses the results of independent 2D trackers. We also present performance analysis of the base tracker along Quality-of-Service (QoS) and Quality-of-Information (QoI) metrics, and study QoS vs. QoI trade-offs between the proposed tracker variations. Finally, we demonstrate our tracker in a real-life scenario using a camera network deployed in a building.

Visual control with adaptive dynamical compensation for 3D target tracking by mobile manipulators

In this paper an image-based dynamic visual feedback control for mobile manipulators is presented to solve the target tracking problem in the 3D-workspace. The design of the whole controller is based on two cascaded subsystems: a minimum norm visual kinematic controller which complies with the 3D target tracking objective, and an adaptive controller that compensates the dynamics of the mobile manipulator. Both the kinematic controller and the adaptive controller are designed to prevent from command saturation. Robot commands are defined in terms of reference velocities. Stability and robustness are proved by using Lyapunov’s method. Finally, experimental results are presented to confirm the effectiveness of the proposed visual feedback controller.

Real‐time 3D target tracking in MRI guided focused ultrasound ablations in moving tissues

Magnetic resonance imaging-guided high intensity focused ultrasound is a promising method for the noninvasive ablation of pathological tissue in abdominal organs such as liver and kidney. Due to the high perfusion rates of these organs, sustained sonications are required to achieve a sufficiently high temperature elevation to induce necrosis. However, the constant displacement of the target due to the respiratory cycle render continuous ablations challenging, since dynamic repositioning of the focal point is required. This study demonstrates subsecond 3D high intensity focused ultrasound-beam steering under magnetic resonance-guidance for the real-time compensation of respiratory motion. The target is observed in 3D space by coupling rapid 2D magnetic resonance-imaging with prospective slice tracking based on pencil-beam navigator echoes. The magnetic resonance-data is processed in real-time by a computationally efficient reconstruction pipeline, which provides the position, the temperature and the thermal dose on-the-fly, and which feeds corrections into the high intensity focused ultrasound-ablator. The effect of the residual update latency is reduced by using a 3D Kalman-predictor for trajectory anticipation. The suggested method is characterized with phantom experiments and verified in vivo on porcine kidney. The results show that for update frequencies of more than 10 Hz and latencies of less then 114 msec, temperature elevations can be achieved, which are comparable to static experiments.

Use of MV and kV imager correlation for maintaining continuous real-time 3D internal marker tracking during beam interruptions

The integration of onboard kV imaging together with a MV electronic portal imaging device (EPID) on linear accelerators (LINAC) can provide an easy to implement real-time 3D organ position monitoring solution for treatment delivery. Currently, real-time MV–kV tracking has only been demonstrated by simultaneous imagining by both MV and kV imaging devices. However, modalities such as step-and-shoot IMRT (SS-IMRT), which inherently contain MV beam interruptions, can lead to loss of target information necessary for 3D localization. Additionally, continuous kV imaging throughout the treatment delivery can lead to high levels of imaging dose to the patient. This work demonstrates for the first time how full 3D target tracking can be maintained even in the presence of such beam interruption, or MV/kV beam interleave, by use of a relatively simple correlation model together with MV–kV tracking. A moving correlation model was constructed using both present and prior positions of the marker in the available MV or kV image to compute the position of the marker on the interrupted imager. A commercially available radiotherapy system, equipped with both MV and kV imaging devices, was used to deliver typical SS-IMRT lung treatment plans to a 4D phantom containing internally embedded metallic markers. To simulate actual lung tumor motion, previous recorded 4D lung patient motion data were used. Lung tumor motion data of five separate patients were inputted into the 4D phantom, and typical SS-IMRT lung plans were delivered to simulate actual clinical deliveries. Application of the correlation model to SS-IMRT lung treatment deliveries was found to be an effective solution for maintaining continuous 3D tracking during ‘step’ beam interruptions. For deliveries involving five or more gantry angles with 50 or more fields per plan, the positional errors were found to have ⩽1 mm root mean squared error (RMSE) in all three spatial directions. In addition to increasing the robustness of MV–kV tracking against beam interruption, it was also found that use of correlation can be an effective way of lowering kV dose to the patient and for increasing kV image quality by reduction of MV scatter interference.

Sensor fusion-based visual target tracking for autonomous vehicles

In this ariticle, a data fusion based algorithm is proposed to identify and track moving objects for autonomous vehicle navigation. It is a challenging problem because both the object and the cameras are moving. Here, the optical flow vector field, color features, and stereo pair disparities are used as visual features, while the vehicle’s motion-sensor data are used to determine the cameras’ motion. We propose a data fusion algorithm which integrates information obtained from different visual cues and the vehicle’s motion-sensor data for target-tracking. The fusion algorithm determines the velocity and position of the target in the 3D world coordinates. Next, we present a detailed description of the three-dimensional (3D) target-tracking algorithm using an extended Kalman filter. Experimental results are presented to demonstrate the performance of the proposed scheme using different natural image sequences.

Vision based data fusion for autonomous vehicles target tracking using interacting multiple dynamic models

In this paper, a novel algorithm is proposed for the vision-based object tracking by autonomous vehicles. To estimate the velocity of the tracked object, the algorithm fuses the information captured by the vehicle’s on-board sensors such as the cameras and inertial motion sensors. Optical flow vectors, color features, stereo pair disparities are used as optical features while the vehicle’s inertial measurements are used to determine the cameras’ motion. The algorithm determines the velocity and position of the target in the world coordinate which are then tracked by the vehicle. In order to formulate this tracking algorithm, it is necessary to use a proper model which describes the dynamic information of the tracked object. However due to the complex nature of the moving object, it is necessary to have robust and adaptive dynamic models. Here, several simple and basic linear dynamic models are selected and combined to approximate the unpredictable, complex or highly nonlinear dynamic properties of the moving target. With these basic linear dynamic models, a detailed description of the three-dimensional (3D) target tracking scheme using the Interacting Multiple Models (IMM) along with an Extended Kalman Filter is presented. The final state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed fusion based IMM tracking algorithm is demonstrated through extensive experimental results.

Issues for the Assessment of Visuospatial Skills in Older Adults Using Virtual Environment Technology

Virtual Environment (VE) technology offers clinical assessment and rehabilitation options that are currently not available using traditional neuropsychological methods. Advancements in this type of immersive information technology could produce tools that enhance the scientific study of human cognitive/functional processes and improve our capacity to more accurately assess and treat impairments found in persons with central nervous system (CNS) dysfunction. Through the creation of dynamic three-dimensional (3D) stimulus environments, in which all behavioral responding can be recorded, VE technology offers the possibility to more sensitively address a range of age-related CNS disorders including Alzheimer's Disease, Vascular Dementia, Parkinson's Disease, and stroke. Advances in this area could impact quality of life issues for an increasingly aging world population. The VE Laboratory at the University of Southern California has developed a suite of ImmersaDesk-format, 3D projection-based VEs. These scenarios target assessment of visuospatial skills including visual field-specific reaction time, depth perception, 3D field dependency (virtual rod and frame test), static and dynamic manual target tracking in 3D space, and spatial rotation. The current project tested healthy older adults (ages of 65 and 92). Participants were administered a standard neuropsychological battery and a suite of VE-delivered visuospatial tasks. Issues addressed in this project include: the occurrence of VE-related side effects in healthy older adults; the relationship between performance on VE measures and standard neuropsychological tests; the assessment of gender specific performance differences; the relationship between immersive tendencies, presence ratings, and VE performance in older adults; learning and generalization; and VE visuospatial performance differences between younger and older participants. This article will address the motivation, rationale, and relevant issues for use of VEs with older adults. A description of our VE system/methodology in the context of a recent study targeting assessment and possible rehabilitation of visuospatial skills with this population will then be detailed.

Smart Servoing of a Controllable Range Sensor for Target Tracking: Application to Pedestrians

This paper presents a system for 3D target tracking. A controllable range sensor is servoid in order to focus the attention of the system on the target. The 3D-sensor is a laser telemeter based range finder of which the laser beam is deflected by two flat mirrors. The deviations of the mirrors are fully programmable and can be dynamically controlled by an application program. The data from the sensor are passed to a Kalman filter which predicts the next location of the target. The predicted location of the target allows to define an area which is scanned by the laser beam to obtain a 3D image. This system is applied to pedestrian tracking in a road environment.