Motion Object Research Articles

Efficient dynamic 3D shape measurement technique for resisting motion-induced error

The phase-shifting profilometry is usually used to acquire dynamic 3D scenarios owning to these merits of dense 3D points, high speed, and simple devices. However, the object in motion will introduce an unknown phase shift, inducing the motion error in 3D shape. Eliminating the motion error with high efficiency is still a significant challenge. This paper analyzes the model of motion error and proposes a novel fringe projection strategy to resist the motion error. Unlike the traditional methods, typically compensating for the motion error with supplementary computing, the proposed method can directly obtain the accurate phase since the designed fringe projection strategy possesses the function of resisting motion error. Therefore, it is more efficient without the post-processing procedure. Simulations and experiments reveal that the proposed method can significantly improve efficiency with reasonable accuracy.

Вращение супермолекул вокруг промежуточной оси инерции

In the problem of the inertial rotation of molecular objects, only kinematic relations for the nodes of the molecular structure are evolutionary. These relations determine the position of the atoms of a supermolecule depending on the instantaneous angular velocity of the object in inertial motion. All other relations are algebraic, since they are integrals of the equations of rotational motion. The latter relations include both the projections of the angular velocities of the molecule and the instantaneous coordinates of the atoms. Within the framework of the fourth-order Runge-Kutta scheme, each time step is divided into four positions. Initially, in each of these positions, new values of coordinates are determined or the initial coordinates of atoms at the first position of the first time step are used. After the coordinates are found, in the same position, the projections of angular velocities of the supermolecule are obtained from conservation relations for the projections of the angular momentum. Based on the values of the coordinates in four positions, the coordinates on a new time layer are recalculated. After that, solving the system of three linear algebraic equations according to Cramer's rule, the projections of angular velocities at a new time step are determined. Then, the cycle is repeated. During the inertial rotation, the kinetic energy of an object is conserved. Verification of the calculated kinetic energy shows that the result is obtained with machine accuracy. Further, the constructed calculation scheme is used to study the Louis Poinsot instability. The full range of the considered instability for a fullerene C100 (C1 symmetry) is presented.

Distractor-aware Event-based Tracking.

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.

Slope Collapse Detection Method Based on Deep Learning Technology

So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural network with an attention mechanism to recognize the detected motion object, thus eliminating the interference motion and increasing the detection accuracy rate. Experimental results on the artificial data and relevant scene data show that the proposed detection method can effectively identify the slope collapse, which has its applicative value and brilliant prospect.

TIF: Trajectory and Information Flow Coupling Mechanism for Behavior Analysis in Autonomous Driving

The significant achievements have been made in crowd detection and tracking due to the advancement of artificial intelligence in the autonomous driving. However, the image-based methods have strict requirements for the collection conditions of video, and the development of the new generation of flexible fabrics has become potential sensors to perceive context. In this paper, an intelligent fabric space enabled by multi-sensing sensors is established to track the motion objects. We propose a behavior analysis pipeline including the modules of data preparation, trajectory coupling, motion scenario segmentation, and motion pattern measurement to capture the crowd information from micro-level and macro-level over the intelligent fabric space. After making preprocess for the multi-sensing data, a coupling mechanism is formulated to fuse the video-based trajectory and fabric-based trajectory. And an automatic motion scenario segmentation model divides the surrounding scenario into main-crowd, sub-crowd, and background according to the motion behavior. Further, we define measurement metrics to analyze the motion pattern for the different crowds. Extensive experiments prove that our proposed methods effectively fuse multiple trajectories and realize the crowd segmentation and the motion description. This will greatly help autonomous vehicles and control system perceive the surrounding pedestrians and the environment to make precise driving decisions.

XGAPS: a sub-arcsec cross-match of galactic plane surveys

ABSTRACT We present a sub-arcsec cross-match of Gaia Data Release 3 (DR3) against the INT Galactic Plane Surveys (IGAPS) and the United Kingdom Infrared Deep Sky Survey (UKIDSS). The resulting cross-match of Galactic Plane Surveys (XGAPS) provides additional precise photometry (URGO, g, r, i, Hα, J, H, and K) to the Gaia photometry. In building the catalogue, proper motions given in Gaia DR3 are wound back to match the epochs of the IGAPS constituent surveys (INT Photometric HαSurvey of the Northern Galactic Plane, IPHAS, and the UV-Excess Survey of the Northern Galactic Plane, UVEX) and UKIDSS, ensuring high-proper motion objects are appropriately cross-matched. The catalogue contains 33 987 180 sources. The requirement of >3σ parallax detection for every included source means that distances out to 1–1.5 kpc are well covered. In producing XGAPS, we have also trained a Random Forest classifier to discern targets with problematic astrometric solutions. Selection cuts based on the classifier results can be used to clean colour-magnitude and colour–colour diagrams in a controlled and justified manner, as well as producing subsets of astrometrically reliable targets. We provide XGAPS as a 111 column table. Uses of the catalogue include the selection of Galactic targets for multi-object spectroscopic surveys as well as identification of specific Galactic populations.

An efficient neural network for low sampling computational ghost imaging based on EMNIST training

In this paper, we propose a new network to achieve low-sampling computational ghost imaging. The proposed neural network that combines ResNeXt, eHoloNet and spatial attention mechanism is efficient for object reconstruction just based on EMNIST training, which is much simpler and time-saving. The generalization of the neural network is verified with multi-slits as well as complex object. Both simulated and experimental results show that the proposed network can give an effective reconstruction at 0.7% sampling rate. The neural network in this work is of great significance to ghost imaging in a wider application scenarios, such as real-time imaging and dynamic detection of motion object.

Measuring the average velocity and acceleration of a moving object on an inclined plane using a magnetic sensor on a smartphone

This paper demonstrates an alternative method to measure the time interval of a moving object in linear motion using the magnetic sensor of a smartphone. The smartphone is stationary and records the magnetic field as a function of time from a moving object with attached magnetic buttons. The value of time interval is then measured and both velocity and acceleration are calculated for the moving object. This method prevents the smartphone from being damaged. The proposed experiment is easy to set up with available objects, and students can conduct this experiment at home.

Measuring the kinematic parameters of a rotating object in circular motion using the magnetometer of a smartphone

This paper describes a method to measure the kinematic parameters of a rotating object in circular motion using the magnetometer of a smartphone. A wheel with four legs had magnetic buttons attached to the ends of the legs. The smartphone was placed on the ground to measure the magnetic field when the wheel rotated. The experimental data can be calculated to obtain an angular position, an angular velocity, and an angular acceleration. The results show that the smartphone can be a useful tool to determine kinematic parameters in circular motion. The experiment is easy to set up using simple available objects, and students can conduct this experiment at home.

An Efficient Method for Calculating the Doppler Spectrum of an Arbitrarily Shaped Object in Uniform Motion

In the article, an efficient numerical method for calculating the spectrum of the scattering fields of an arbitrarily shaped object in uniform motion is proposed. This method relies on the frame hopping technique stemming from the special theory of relativity (STR). Firstly, the incident monochromatic plane wave is transformed to the comoving inertial frame in which the object is stationary. Next, the scattering fields of the moving object at the sampling time points are obtained in the comoving frame by solving the electromagnetic (EM) scattering problem with the conventional numerical method in the frequency domain. Then, the spectrum of the scattering fields in the frame in which the observer remains stationary is obtained using the discrete Fourier transform (DFT). The asymptotic expressions for the scattering fields when the moving object is far away from the observation point are derived, by applying which the computation efficiency could be greatly improved.

Destinations in Mind: Portraying Places on the Roman Empire’s Souvenirs By Kimberly Cassibry. Oxford: Oxford University Press 2021. Pp. xvii + 278. $99. ISBN 9780190921897 (cloth).

<i>Destinations in Mind: Portraying Places on the Roman Empire’s Souvenirs</i> By Kimberly Cassibry. Oxford: Oxford University Press 2021. Pp. xvii + 278. $99. ISBN 9780190921897 (cloth).

Investigating The Effects Of 3-Dimensional Motion Object Tracking (3D-MOT) Training On In-game College Soccer Performance

Investigating The Effects Of 3-Dimensional Motion Object Tracking (3D-MOT) Training On In-game College Soccer Performance

Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos

ABSTRACT Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency.

Detection of Change in Body Motion With Background Construction and Silhouette Orientation

Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity

SiamGauss: Siamese region proposal network with Gaussian head for visual object tracking

We propose SiamGauss, a Siamese region proposal network with a Gaussian head for single-target visual object tracking for aerial benchmarks. Visual tracking in aerial videos faces unique challenges due to the large field of view resulting in small size objects, similar looking objects (confusers) in close proximity, occlusions, and fast motion due to simultaneous object and camera motion. In Siamese tracking, a cross-correlation ration is performed in the embedding space to obtain a similarity map of the target within a search frame, which is then used to localize the target. The proposed Gaussian head helps suppress the activation produced in the similarity map on confusers present in the search frame during training while boosting the confidence on the target. This activation suppression improves the confuser awareness of our tracker. In addition, improving the activation on the target helps maintain tracking consistency in fast motion. Our proposed Gaussian head is only applied during training and introduces no additional computational overhead during inference while tracking. Thus, SiamGauss achieves fast runtime performance. We evaluate our method on multiple aerial benchmarks showing that SiamGauss performs favorably with state-of-the-art trackers while rating at a frame rate of 96 frames per second.

Discovering vanishing objects in POSS I red images using the Virtual Observatory

ABSTRACT In this paper, we report a search for vanishing sources in POSS I red images using virtual observatory (VO) archives, tools, and services. The search, conducted in the framework of the VASCO project, aims at finding POSS I (red) sources not present in recent catalogues like Pan-STARRS DR2 (limiting magnitude r = 21.4) or Gaia EDR3 (limiting magnitude G = 21). We found 298 165 sources visible only in POSS I plates, out of which 288 770 had a cross-match within 5 arcsec in other archives (mainly in the infrared), 189 were classified as asteroids, 35 as variable objects, 3592 as artefacts from the comparison to a second digitization (Supercosmos), and 180 as high proper motion objects without information on proper motion in Gaia EDR3. The remaining unidentified transients (5399) as well as the 172 163 sources not detected in the optical but identified in the infrared regime are available from a VO compliant archive and can be of interest in searches for strong M-dwarf flares, high-redshift supernovae, asteroids, or other categories of unidentified red transients. No point sources were detected by both POSS-I and POSS-II before vanishing, setting the rate of failed supernovae in the Milky Way during 70 yr to less than one in one billion.

PFD-SLAM: A New RGB-D SLAM for Dynamic Indoor Environments Based on Non-Prior Semantic Segmentation

Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances.

Multi View Video Summarization Using RNN and SURF Based High Level Moving Object Feature Frames

Multi-View Video summarization is a process to ease the storage consumption that facilitates organized storage, and perform other mainline videos analytical task. This in-turn helps quick search or browse and retrieve the video data with minimum time and without losing crucial data. In static video summarization, there is less challenge in time and sequence issues to rearrange the video-synopsis. The low-level features are easy to compute and retrieve. But for high-level features like event detection, emotion detection, object recognition, face detection, gesture detection, and others requires the comprehension of the video content. This research is to propose an approach to over- come the difficulties in handling the high-level features. The distinguishable contents from the videos are identified by object detection and feature-based area strategy. The major aspect of the proposed solution is to retrieve the attributes of a motion source from a video frame. By dividing the details of the object that are available in the video frame wavelet decomposition are achieved. The motion frequency scoring method records the time of motions in the video. The frequency motion feature of video usage is a challenge given the continuous change of objects shape. Therefore, the object position and corner points are spotted using Speeded Up Robust Features (SURF) feature points. Support vector machine clustering extracts keyframes. The memory-based re- current neural network (RNN) recognizes the object in the video frame and remembers a long sequence. RNN is an artificial neural network where nodes form a temporal relationship. The attention layer in the proposed RNN network extracts the details about the objects in motion. The motion objects identified using the three video clippings is finally summarized using video summarization algorithm. To perform the simulation, MATLAB R 2014b software was used.

Motion control for a differential vehicle with variable point of interest. Application: Smart cane control

This article addresses an unified solution to the trajectory tracking and path following problems for differential drive mobile robots (DDMR) considering a point of interest (PoI) with variable location relative to the vehicle. The mobile robot is modeled with an extended kinematic model avoiding typical singularities of this kind of vehicles, and allowing a straightforward definition of the corresponding inverse kinematics controller (IKC). This classical IKC fulfills the control objective with exponential error convergence but with the shortcoming of generating backward navigation when the PoI is located behind the DDMR, which is undesirable in some practical applications where the forward navigation must be preserved. This situation is theoretically analyzed, concluding that even though both forward and backward navigations correspond to equilibrium points of the closed loop, the stability of the forward navigation requires a PoI located in front of the DDMR, and the stability of the backward navigation requires a PoI located behind the DDMR. Finally, the article presents novel alternative controllers in order to always fulfill the motion objectives with stable forward navigation. Simulation results are presented to show the performance of the proposed controllers, and a real application of a robotic cane guiding its user is experimentally developed.

A new approach to representations of homothetic motions in Lorentz space

Abstract In this study, Rodrigues parameters have been first calculated for a homothetic rotation around spacelike and timelike axis using one parameter homothetic motions in Lorentz 3-space E 1 3 ${\mathbb{E}}_{1}^{3}$ . The behavior of the vectors during the homothetic rotation, which is the increase or decrease (about size of one of the objects in motion) notion, has been investigated as a three dimensional shape with the help of Cinema 4D program. The behavioral differences have been observed on figures. Then, Lorentz motions that corresponding to the homothetic rotation matrices in terms of spacelike and timelike axes have been examined and expressed in E 1 3 ${\mathbb{E}}_{1}^{3}$ . Some definitions, theorems, corollaries and three dimensional figures have been given in Lorentz 3-space.