3D Motion Trajectories Research Articles

Retraction Note: 3D motion trajectory prediction based on optical image remote sensing data in sports training simulation

Retraction Note: 3D motion trajectory prediction based on optical image remote sensing data in sports training simulation

Research on Submersible Safety Operations Based on the Basic Information 3D Motion Trajectory and Entropy Weight Method Models

Research on Submersible Safety Operations Based on the Basic Information 3D Motion Trajectory and Entropy Weight Method Models

Tracking the extensive three-dimensional motion of single ions by an engineered point-spread function

Three-dimensional (3D) imaging of individual atoms is a critical tool for discovering new physical phenomena and developing new technologies in microscopic systems. However, the current single-atom-resolved 3D imaging methods are limited to static circumstances or a shallow detection range. Here, we demonstrate a generic dynamic 3D imaging method to track the extensive motion of single ions by exploiting the engineered point-spread function (PSF). We show that the image of a single ion can be engineered into a helical PSF, thus enabling single-snapshot acquisition of the position information of the ion in the trap. A preliminary application of this technique is demonstrated by recording the 3D motion trajectory of a single trapped ion and reconstructing the 3D dynamical configuration transition between the zig and zag structures of a 5-ion crystal. This work opens the path for studies on single-atom-resolved dynamics in both trapped-ion and neutral-atom systems.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy, high resolution and high efficiency. However, it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm. To address these challenges, this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography. Firstly, background difference algorithm is utilized to extract the center and area of each fragment in the image sequence. Subsequently, a multi-object tracking (MOT) algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm. To reconstruct 3D motion trajectories, a global stereo trajectories matching strategy is presented, which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting. Finally, the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×103 fragments in a field of view (FOV) of 3.2 m×2.5 m, and the accuracy of the velocity estimation can achieve 98.6%.

An investigation of methods to improve the accuracy of classical dance steps based on machine vision recognition technology

Abstract Methods that use motion capture data to analyze human behavior are highly interpretable and offer significant advantages in vision-based dance step analysis. In this study, we propose a fusion feature extraction method that describes the rotational information embedded in the skeleton and combines the advantages of single-skeleton feature extraction and Li group feature extraction methods for recognition. The feasibility and validity of the proposed model are verified by conducting recognition experiments on BVH data of classical dance steps performed by subjects. In the ablation experiments, the average accuracy of the classical dance step recognition model based on fused features for step movement recognition is improved by 3.01% and 1.86% compared with the neural network model and the Lie group network model, respectively. It has been proven that adding rotation information to skeletal features can effectively differentiate 3D motion trajectories in similar dance steps. Furthermore, the dance movement trajectories derived from this model are very clear and can be utilized to direct the correct joint point positions throughout the movement. The recognition accuracy of the fusion feature extraction-based recognition method for all seven classical dance basic foot positions is greater than 90%, which reflects the accuracy of the proposed machine vision model in recognizing classical dance steps.

RETRACTED ARTICLE:3D motion trajectory prediction based on optical image remote sensing data in sports training simulation

RETRACTED ARTICLE:3D motion trajectory prediction based on optical image remote sensing data in sports training simulation

Deep learning-based activity-aware 3D human motion trajectory prediction in construction

Predicting human motion is a critical requirement in various applications, with particular significance in the construction sector. This task presents significant challenges due to the diverse nature of human actions and the complexities of converting 2D image coordinates to real-world space. In response to these challenges, this paper introduces an innovative deep learning-driven approach to forecast human motion trajectories, with a novel emphasis on activity recognition to improve predictive accuracy. The method utilizes a multi-camera system to extract 2D joint locations, which are then fused using a particle filter technique for 3D pose generation. Using 3D data, deep learning models are developed to first recognize the activity class, and then take it as auxiliary information for predicting the motion trajectory. Through a comprehensive experiment, we evaluated the proposed methodology. While the main innovation of the proposed approach lies in the incorporation of deep learning-based activity recognition into the trajectory prediction system, the experiment results revealed the activity-aware system’s capability to enhance prediction performance by a minimum of 6.4% and up to 16.6% in short-term forecasts, in compare with a conventional approach. Additionally, we analyzed the effects of varying time windows and joint selections on predictive outcomes across diverse scenarios and discussed the implications of these findings. By enhancing the prediction of human motion, this approach holds promise in improving workspace safety while encouraging effective interactions within complex environments.

Intrafraction tumor motion monitoring and dose reconstruction for liver pencil beam scanning proton therapy

Pencil beam scanning (PBS) proton therapy can provide highly conformal target dose distributions and healthy tissue sparing. However, proton therapy of hepatocellular carcinoma (HCC) is prone to dosimetrical uncertainties induced by respiratory motion. This study aims to develop intra-treatment tumor motion monitoring during respiratory gated proton therapy and combine it with motion-including dose reconstruction to estimate the delivered tumor doses for individual HCC treatment fractions. Three HCC-patients were planned to receive 58 GyRBE (n=2) or 67.5 GyRBE (n=1) of exhale respiratory gated PBS proton therapy in 15 fractions. The treatment planning was based on the exhale phase of a 4-dimensional CT scan. Daily setup was based on cone-beam CT (CBCT) imaging of three implanted fiducial markers. An external marker block (RPM) on the patient's abdomen was used for exhale gating in free breathing. This study was based on 5 fractions (patient 1), 1 fraction (patient 2) and 6 fractions (patient 3) where a post-treatment control CBCT was available. After treatment, segmented 2D marker positions in the post-treatment CBCT projections provided the estimated 3D motion trajectory during the CBCT by a probability-based method. An external-internal correlation model (ECM) that estimated the tumor motion from the RPM motion was built from the synchronized RPM signal and marker motion in the CBCT. The ECM was then used to estimate intra-treatment tumor motion. Finally, the motion-including CTV dose was estimated using a dose reconstruction method that emulates tumor motion in beam's eye view as lateral spot shifts and in-depth motion as changes in the proton beam energy. The CTV homogeneity index (HI) The CTV homogeneity index (HI) was calculated as . The tumor position during spot delivery had a root-mean-square error of 1.3mm in left-right, 2.8mm in cranio-caudal and 1.7mm in anterior-posterior directions compared to the planned position. On average, the CTV HI was larger than planned by 3.7%-points (range: 1.0-6.6%-points) for individual fractions and by 0.7%-points (range: 0.3-1.1%-points) for the average dose of 5 or 6 fractions. A method to estimate internal tumor motion and reconstruct the motion-including fraction dose for PBS proton therapy of HCC was developed and demonstrated successfully clinically.

An Estimation and Compensation Method for Motion Trajectory Error in Bistatic SAR

Bistatic synthetic aperture radar (BiSAR) has drawn increasing attention in recent studies benefiting from its ability for forward-looking imaging, its capability of receiver radio silence and its resistance to jamming. However, the motion trajectory error compensation of BiSAR is a challenging task due to multiple error sources and complex effects. In this paper, an estimation and compensation method for three-dimensional (3D) motion trajectory error of BiSAR is proposed. In this method, the Doppler error of multiple scattering points is estimated firstly by using the time–frequency analysis method. Next, a local autofocus process is introduced to improve the Doppler error estimation accuracy. Then, the 3D trajectory error of BiSAR is estimated by solving a series of linear equations of the trajectory error and the Doppler error with the least squares method, and a well-focused BiSAR image is produced by using the corrected 3D trajectories. Finally, simulation and experiment results are presented to demonstrate the effectiveness of the proposed method.

BgRT Motion Management Maintains Target Dose Coverage for Respiratory and Non-Respiratory Motion

<h3>Purpose/Objective(s)</h3> Biology-guided radiotherapy (BgRT) uses outgoing emissions after administration of an injected radiotracer to dynamically guide the delivery of radiation treatment to PET-avid targets. In this study, we evaluated the tracking performance of FDG-guided BGRT for two potential types of motion management (respiratory and non-respiratory (gastrointestinal)) while exploring different target shapes: spherical and non-spherical. BgRT is designed to ensure target coverage while keeping organ-at-risk (OAR) doses low. <h3>Materials/Methods</h3> A large custom anthropomorphic phantom with 2 robotic articulating arms was used to place a target and organ-at-risk inside a 27-liter water filled cavity. The targets consisted of an integrated ion chamber inside a 3D printed 22 mm sphere or a small non-spherical ovoid shape filled with FDG. The hot FDG-filled OAR consisted of either a 3D printed large C-shape annulus (mimicking a heart), large ovoid (mimicking a kidney), or a 30 mm sphere, all with integrated ion chambers. Both the 3D printed target and the OAR were filled with an 8:1 concentration ratio versus the water background. Three different 10 Gy/fraction treatment plans with two different target shapes and realistic nearby OARs were created with a 5 mm margin added to the CTV to generate the PTV. 3D printed targets and OARs were loaded with EBT-XD radiographic film. A 3D elliptical motion trajectory with breathing induced hysteresis with a maximum amplitude of +/- 12 mm was used for the respiratory motion model. An asymmetric slow-moving drift waveform with long-step shifts of +13/-5mm mm was used for the non-respiratory (gastrointestinal) motion model. Dosimetric results were evaluated based on 2-dimensional comparisons between measured film and planned dose distributions for the target and OAR. BgRT must maintain a prescription dose margin greater than 2 mm on the CTV for the three different motion experiments. Also, ion-chamber point dose and maximum film dose on the OAR must be within bounds predicted by the treatment planning system. <h3>Results</h3> BgRT was able to meet minimum margin requirements (a margin loss < 3mm) and meet prescription dose coverage in both motion management scenarios while maintaining OAR dose limits within the plan predicted bounds (see Table 1). <h3>Conclusion</h3> BgRT is able to provide motion management in complex cases that is agnostic to the type of motion. BgRT is able to maintain the PTV coverage with less than 3 mm reduction from the original 5mm margin.

Optimization of 3D Virtual Reality Technology in High School Physics Direct-Type Teaching

Virtual reality technology is a computer simulation that allows the creation and experience of virtual worlds, using computers to generate a simulated environment into which the user is immersed. Through a theoretical exposition of 3D virtual reality technology, the elements of teaching and learning and virtual reality technology are analyzed in terms of abstract virtual cognition and concrete modeling techniques, and the forms of application of virtual reality technology in high school physics teaching are described, along with associated educational technology theories. The era of core literacy development poses new challenges to basic teaching and requires new teaching methods and approaches to adapt. The development of 3D virtual reality technology has provided new opportunities for teaching reform. Firstly, the unique charm of virtual reality technology itself has deeply attracted many experts and scholars to explore its application in pedagogy. Secondly, the scale and effect of virtual reality technology in pedagogy applications are still far from the ideal goal in people’s mind, and further exploration and research are needed in the theoretical support of educational technology research. Thirdly, in the process of high school basic teaching, on the one hand, basic experiments play a crucial role, and on the other hand, transforming abstract basic principles and concepts into concrete visible and operable teaching methods is also an important direction for its future development. Combined with the development tools and software to build processes and models, the knowledge of electricity and mechanics in high school physics is modeled in 3Ds MAX and Unity3D to provide technical support for the application of virtual reality in high school physics teaching and to promote the reform of high school physics teaching. Based on an in-depth analysis of virtual reality technology in the construction of teaching resources, this paper explores the feasibility of using software systems and hardware facilities of 3D virtual reality technology in high school physics teaching, such as experimental demonstration, geometric modeling, and 3D graphic transformation. It fits the cognitive level of high school students, constructs 3D virtual scenes and virtual object motion trajectory models that meet the requirements of the high school physics curriculum, and promotes the application of virtual reality technology in high school physics teaching and visualizing abstract physics knowledge, with clear student understanding and significant improvement in learning efficiency.

Residual Neural Network and Long Short-Term Memory–Based Algorithm for Estimating the Motion Trajectory of Inertial Measurement Units

Motion trajectory is a technology used in smart medicine, rehabilitation engineering, and interactive games. Studies have proposed several motion trajectory tracking systems based on inertial measurement units (IMUs). However, these systems are beset by problems such as low adaptability to various activities and the capacity for only two-dimensional trajectory estimation. To solve these problems, this study proposed a three-dimensional (3D) motion trajectory tracking system based on a residual neural network (ResNet) and bidirectional long short-term memory (Bi-LSTM) to estimate the user’s trajectory of daily activities. A participant was asked to wear the IMU device and perform two types of specified activities, namely hand-movement tasks and walking. Nine degrees of freedom data from the IMUs were collected during these activities and input into the deep learning model, which combines ResNet and Bi-LSTM, to detect features and predict the motion trajectory. Several experiments were conducted to obtain the optimal system architecture and parameter settings and to evaluate the accuracy of motion trajectory estimation. The accuracy of the proposed system was also compared with those in other studies. The results demonstrated that the relative trajectory error of walking is 0.81 m and that of general cases is 0.54 m, which all outperforms the other related studies. The evidence indicated that the proposed system can predict the 3D motion trajectory of daily activities accurately and can be easily applied to track the motion of most daily activities.

Fluid Equation-Based and Data-Driven Simulation of Special Effects Animation

This paper analyzes the simulation of special effects animation through fluid equations and data-driven methods. This paper also considers the needs of computer fluid animation simulation in terms of computational accuracy and simulation efficiency, takes high real-time, high interactivity, and high physical accuracy of simulation algorithm as the research focus and target, and proposes a solution algorithm and acceleration scheme based on deep neural network framework for the key problems of simulation of natural phenomena including smoke and liquid. With the deep development of artificial intelligence technology, deep neural network models are widely used in research fields such as computer image classification, speech recognition, and fluid detail synthesis with their powerful data learning capability. Its stable and efficient computational model provides a new problem-solving approach for computerized fluid animation simulation. In terms of time series reconstruction, this paper adopts a tracking-based reconstruction method, including target tracking, 2D trajectory fitting and repair, and 3D trajectory reconstruction. For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. The experimental results show that in terms of spatial reconstruction, the matching method proposed in this paper is more accurate compared with the traditional stereo matching algorithm; in terms of time series reconstruction, the error of target tracking reduced. Finally, the 3D motion trajectory of the point feature object and the motion pattern at a certain moment are shown, and the method in this paper obtains more ideal results, which proves the effectiveness of the method.

Optimization of Fresnel-zones-based Double Helix Point Spread Function and measurement of particle diffusion coefficient

In the field of microscopic imaging, the double-helix point spread function (DH-PSF) based on the Fresnel zones is often used to locate the three-dimensional position of particles. The lower transfer function efficiency makes it difficult to track the trajectory of moving particles in real-time, especially when the particles are far from the initial focal plane. We proposed a method based on the iterative Fourier transform algorithm and the Fresnel approximate imaging to optimize the phase which is used to generate the DH-PSF. The optimized DH-PSF has been significantly improved throughout the rotation process, and the maximum peak intensities of some positions have been increased by 82.31%. The main lobe strength of the optimized DH-PSF has been significantly improved, which was proved in vector diffraction simulation and practical experiments. After that, the optimized DH-PSF was used to track the 3D motion trajectories of nanoparticles with different shapes, and their diffusion coefficients were calculated. Experiments show that the optimized DH-PSF can quickly and accurately obtain the 3D motion trajectories of nanoparticles of various shapes and accurately calculate their diffusion coefficients in various solutions, which is important for the development of nanomedicine.

Spatial radiation features of circularly polarized tightly focused laser beams colliding with electrons

In the framework of laser-matter interaction and nonlinear Thomson scattering, we investigate the three-dimensional (3D) motion trajectories and spatial radiation characteristics of the colliding motion electrons with tightly focused circularly polarized Gaussian pulses at different intensities. Combined with the 3D spatial images and the analysis of specific two-dimensional unfolded images, we discover that the amplitude of the motion trajectory increases steadily with increasing laser intensity, the peak radiation power increases rapidly, and the peak radiation azimuth decreases slowly. In addition, by studying the electron motion under the high-intensity () laser field, we find that the electron motion is retrograde twice when interacting with pulsed laser once. Lastly, by extracting the direction and energy of spatial radiation, we indicate that the existence of a specific laser intensity can make the peak radiation azimuth and which intienity provides the maximum conversion rate of laser energy to the peak power of radiation.

A MODULAR MOBILE MAPPING PLATFORM FOR COMPLEX INDOOR AND OUTDOOR ENVIRONMENTS

Abstract. In this work we present the development of a prototype, mobile mapping platform with modular design and architecture that can be suitably modified to address effectively both outdoors and indoors environments. Our system is built on the Robotics Operation System (ROS) and utilizes multiple sensors to capture images, pointclouds and 3D motion trajectories. These include synchronized cameras with wide angle lenses, a lidar sensor, a GPS/IMU unit and a tracking optical sensor. We report on the individual components of the platform, it’s architecture, the integration and the calibration of its components, the fusion of all recorded data and provide initial 3D reconstruction results. The processing algorithms are based on existing implementations of SLAM (Simultaneous Localisation and Mapping) methods combined with SfM (Structure-from-Motion) for optimal estimations of orientations and 3D pointclouds. The scope of this work, which is part of an ongoing H2020 program, is to digitize the physical world, collect relevant spatial data and make digital copies available to experts and public for covering a wide range of needs; remote access and viewing, process, design, use in VR etc.

Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild.

Bundle adjustment jointly optimizes camera intrinsics and extrinsics and 3D point triangulation to reconstruct a static scene. The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras. We present a spatiotemporal bundle adjustment framework that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating static 3D points, as well as sub-frame temporal alignment between cameras and computing 3D trajectories of dynamic points. Key to our joint optimization is the careful integration of physics-based motion priors within the reconstruction pipeline, validated on a large motion capture corpus of human subjects. We devise an incremental reconstruction and alignment algorithm to strictly enforce the motion prior during the spatiotemporal bundle adjustment. This algorithm is further made more efficient by a divide and conquer scheme while still maintaining high accuracy. We apply this algorithm to reconstruct 3D motion trajectories of human bodies in dynamic events captured by multiple uncalibrated and unsynchronized video cameras in the wild. To make the reconstruction visually more interpretable, we fit a statistical 3D human body model to the asynchronous video streams. Compared to the baseline, the fitting significantly benefits from the proposed spatiotemporal bundle adjustment procedure. Because the videos are aligned with sub-frame precision, we reconstruct 3D motion at much higher temporal resolution than the input videos. Website: http://www.cs.cmu.edu/~ILIM/projects/IM/STBA.

3D Motion Trajectories Recognition using Optimal Set of Geometric Primitives, Angular and Statistical Features

This paper presents the 3D motion trajectories (lower case 3D alphabetic characters) recognition using optimal set of geometric primitives, angular and statistical features. It has been observed that the different combinations of these features have not been used in the literature for recognition of 3D characters. The standard dataset named “CHAR3D” has been used for analysis purpose. The dataset consists of 2858 character samples and each character sample is 3 dimensional pen tip velocity trajectory. In this dataset only single pen down segmented characters have been considered. The recognition has been performed using Random Forest (RF) and multiclass support vector machine (SVM) classifier on the optimal subset of extracted features. The best obtained recognition accuracy of 83.4% has been recorded using 3D points, angular and statistical features at 10 fold cross validation using SVM classifier. Moreover, the highest recognition accuracy of 96.88% has been recorded using an optimal subset of 32 dimensional features namely, geometric primitives, angular and statistical features at 10 fold cross validation by RF classifier.

Headlight recognition for night-time traffic surveillance using spatial–temporal information

Vehicle headlights are the important objects especially in the application of night-time traffic surveillance. A common problem of this task is the similarity between the headlights and their reflections on the road. This paper proposes a novel algorithm to construct 3D motion trajectories of headlights and their reflections on the road using both spatial and temporal information. 3D structure tensors are utilized as shape features for recognizing the headlights in various traffic views. Experimental results show that the proposed method performs better than traditional approaches (about 10 $$\%$$) in terms of the F1 score.

Development of Software System for Artificial Joint Biomechanics

In the research of artificial joint biomechanics, it is a common method in the world to evaluate the biomechanical properties of the implanted fixtures through experiments in vitro. The domestic research started late, and the corresponding testing software were lacking. There is still no special software. In this paper, an integrated software test system was designed and built based on the existing hardware system, including:hardware control, data collection, data save, data processing and display. It can directly show the 3D motion trajectory and the angle curve of joints. The system can accurately measure the relative motion angle value, external torque value, and inter pressure value of each joint. It has some reference significance for the development of the artificial joints' evaluation system.