Movement Of Objects Research Articles

Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data

Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's I (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity‐based (considering first/higher‐order and common border) and Euclidean distance‐based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long‐tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow‐related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.

Image schemas in gestural metaphorical scenarios of swearing

Abstract The following study integrates the concept of image schema with the idea of gestural metaphorical scenario, showing how image schematic properties of gestures feed into a pragmatically-oriented gestural metaphorical scenario distributed across different speakers. The study shows that a gestural metaphorical scenario of swearing is based on several image schemas: the cognitively primary object image schema, the source-path-goal image schema, showing the trajectory of movement of an imaginary object, and the blockage image schema, enacting the meaning of not being willing to accept an imaginary object into the speaker’s personal space, effectively leading to blocking its way. The proposed integration also lends more credence to the idea of a multilevel metaphor, where a single metaphor is treated as consisting of various elements at different levels of schematicity. Apart from that, the study shows that the straight image schema, visible in pointing gestures, is a scenario-independent conceptual element surfacing when people use gestures while swearing, projecting a way out of the speaker’s personal space.

Learning analytics in mathematics education: the case of feedback use in a digital classification task on reflective symmetry

Learning Analytics is concerned with the use of data collected in educational settings to support learning processes. We take a Learning Analytics approach to study the use of immediate feedback in digital classification tasks in mathematics. Feedback serves as an opportunity for learning, however its mere existence does not guarantee its use and effectiveness, as what matters is how learners interact with it. Therefore, our research questions are focused on that interaction. The data consisted of 266 object movements for classifying polygons, and 524 shape movements for classifying traffic signs, under the topic of symmetry. Participants included 29 elementary school students (9–12 years old) from Israel and Germany. Analyzing students’ success, feedback use, and the associations between them, we demonstrate how not acting upon feedback is negatively associated with success, and how this undesired behavior slightly reduces along the learning process.

New Possibilities of Representational Expressivity for Mathematical Communication: A Case of DGE in an 8th-Grade Classroom

When implementing digital technologies into mathematics teaching and learning situations, new representational and communication infrastructures arise that allow for new speech acts and actions. These new speech acts and actions are also referred to as 'representational expressivity'. Such expressivity arises in the intersection of representational and communication infrastructures. The paper presents an example of functions as covariation, taken from a task in a Danish 8th-grade classroom. Results show that infrastructures mediate students' mathematical communication. When students drag, continuous treatments between representations take place. The dragging not only influences the transformation and manipulation of mathematical objects – but also mediates the students' language. Loo king at verbs and adjectives, the students' words become more dynamic and focus on the movements of mathematical objects within the digital tool. From a competency point of view, the students must be aware of different mathematical registers in which communication takes place. When working with digital technologies, students must thus be able to co-exercise several mathematical competencies at once.

Implementing hue-saturation-value filter and circle hough transform for object tracking on ball-wheeled robot

The ball-wheeled robot relies on a camera for receiving information on the object to be followed. Object tracing is one of the methods that can be used for detecting object movement. In recognizing objects around it, the robot requires an image analysis process that involves visual perception. Image processing is the process of processing and analyzing images that involves visual perception, and is characterized by input data and output information in the form of images. This is how the robot can see objects around it and then be assisted by computer vision to make a decision. The object tracking method with hue-saturation-value (HSV) colour filtering and shape recognition with circle hough transform (CHT) is applied to the ball-wheeled robot. The front vision of the robot uses HSV colour filtering with various test values to determine the thresholding value, and it was found that the ball could be identified up to a distance of 1,000 cm. To further improve the performance of recognizing the ball object, CHT was applied. It was found that the ball could be identified up to a distance of 700 cm. Furthermore, the ball can be identified in obstructed conditions up to 75%.

REA-FM: automated generation of natural-looking flow maps through river extraction algorithm

ABSTRACT A flow map is a type of thematic visualization that depicts the movement of objects across a geographical space using a tree layout resembling a natural river system. In this paper, we introduce an innovative and automated approach called REA-FM, which leverages the power of the maze-solving algorithm to extract rivers from digital elevation models (DEMs). This enables the creation of flow maps that originate from a single source and extend to multiple destinations. Initially, REA-FM represents the mapping space of a flow map using a DEM. Subsequently, a maze-solving algorithm is adapted to extract flow paths from the destinations to the origin within the DEM data, with constraints on search directions, direction weights, and search ranges based on quality criteria specific to flow maps. To obtain comprehensive flow maps, the maze-solving algorithm is employed iteratively, considering the importance of each flow path, as determined by their respective lengths. These obtained paths are finally rendered smoothly with varying widths using Bézier curves, thereby enhancing the visual aesthetics of the flow map. A comparative evaluation with existing approaches demonstrates that REA-FM can generate natural-looking flow maps with reduced total length and improved node distribution, eliminating node overlaps and edge crossings. Furthermore, the effectiveness of REA-FM is validated through three extension experiments involving heterogeneous mapping spaces and areas with obstacles. Parameter analysis confirms that REA-FM offers intuitive control over the layout of flow maps. Project website: https://github.com/TrentonWei/FlowMap

Doppler detection triggers instantaneous escape behavior in scanning bats

Animals must instantaneously escape from predators for survival, which requires quick detection of approaching threats. Although the neural mechanisms underlying the perception of looming objects have been extensively studied in the visual system, little is known about their auditory counterparts. Echolocating bats use their auditory senses to perceive not only the soundscape, but also the physical environment through active sensing. Although object movement induces both echo delay changes and Doppler shifts, the actual information required to perceive movement has been unclear. Herein, we addressed this question by playing back phantom echoes mimicking an approaching target to horseshoe bats and found that they relied only on Doppler shifts. This suggests that the bats do not perceive object motion in the spatiotemporal dimension (i.e., positional variation), as in vision, but rather take advantage of acoustic sensing by directly detecting velocity, thereby enabling them to respond instantaneously to approaching threats.

Trajectory Interception Classification for Prediction of Collision Scope between Moving Objects

In the fields of autonomous navigation and vehicle safety, accurately predicting potential collision field points between moving objects is a significant challenge. A novel computing technique to enhance trajectory interception analysis is presented in this paper. Our objective is to develop a field model that can accurately forecast collision zones, improving road transportation safety and the use of autonomous cars. Our main contribution is a binary classification model called PCSMO (Prediction of Collision Scope between Moving Objects), which is based on zero-shot learning. Gann angles, which are typically 45 degrees, are used to analyze the trajectories of moving objects. This method is inspired by GANN (Gann Angle Numeric Nomenclature). Compared to earlier techniques, this model more accurately identifies potential collision collision interception zones. The technique computes Gann angles for trajectory analysis and extracts GPS coordinates of moving objects from video data using OpenCV. It offers a more sophisticated comprehension of object movement patterns and points of interception. To assess the precision, recall, F1-score, and prediction accuracy of our model, we employ 10-fold cross-validation. Comparing the PCSMO model to existing models, these metrics demonstrate how well the PCSMO model predicts potential collision zones. Our approach, we discovered, enhances trajectory analysis—a critical component of safer autonomous navigation systems. With potential applications in autonomous vehicle and UAV safety, the PCSMO model improves field interception classification.

Reserching performance of the Dubins machine hybrid model with single separation of control objects

The problem of the performance of a hybrid system, which the number of control objects changes during the operation is considered. One control object (carrier) begins to move. At some point in time, several moving objects are separated from it and sent to specified terminal states (targets). The carrier is represented by a hybrid model of the Dubins machine, allowing trajectories of unlimited curvature. The movement of separated objects is rectilinear with limited speeds and accelerations. The problem of minimizing the time to achieve all goals is solved.

Optimizing the Personnel Position Monitoring System Using the Global Positioning System in Hostage Release

In the contemporary era of globalization, maintaining public order depends on strong security measures. Addressing security challenges, particularly in hostage release scenarios, requires rapid and appropriate responses, highlighting the need for efficient personnel deployment. This research proposes an advanced solution using a GPS Tracking System which uses a sequential method by utilizing digital photos from GPS satellites to monitor the movement of individuals and objects. Specifically applied to the Sandra rescue mission, our research uses the NodeMCU ESP8266 component, which integrates GPS and Wi-Fi functions while considering wind direction. Tests performed demonstrated an impressive success rate of 98.6%, demonstrating the effectiveness of our real-time personnel positioning approach.

Evolution of CNNs in human tracking applications

Tracking the movement of objects in videos or footage from CCTV systems plays an integral role in crime investigations, surveillance, security predictions, and many other domains. Historically, this task was primarily entrusted to dedicated observers or analysts who would be summoned to review pre-recorded footage post-event. With the advent of machine learning and AI, convolutional neural networks (CNNs) have paved the way for computers to augment human capabilities in analyzing streaming videos or archived recordings. Among the various tracking methodologies that machine learning offers, YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Networks), along with its iterations, stand out as some of the most reliable and precise. However, the scope of analysis often extends beyond these techniques. To enhance accuracy and provide an adept classification mechanism, the Deep SORT (Simple Online and Realtime Tracking) algorithm emerges as pivotal. Its synergy with human detection remains a significant area of discussion and will be deliberated upon in this study. This review aims to elucidate the intricacies of these state-of-the-art methods and their interplay in modern tracking systems.

Scalar fields features of video sequences energy characteristics

To improve the methods of spatio-temporal filtering, an approach based on the analysis of finitary images energy spectra can be used. The purpose of this work is to study the possibility of using energy spectra for motion identification in video sequence frames. Each frame of a video sequence is a spatially finite image, the energy spectrum of which contains information about the phase components. This information is of particular importance for determining the location of dynamic objects. The most important information is also contained in the harmonic amplitude increments of the frame energy spectra, i.e. the energy spectrum generates a scalar discrete field. The energy functions describe inter-frame changes in the amplitudes of harmonics of the energy spectrum. To apply energy spectra in motion identification algorithms, it is necessary to analyze the properties of these scalar fields. Scalar fields generated by energy spectra of frames of test video sequences are studied. Test video sequences with an object moving in an arbitrary direction are used. Scalar fields of energy functions are presented graphically. Characteristic regions of scalar fields are identified and their projections are built using isolines. The boundaries of the characteristic areas and the coordinates of extrema of energy functions are determined. From them, all coordinates of the dynamic object and its horizontal/vertical displacements can be found. The dependence of phase-energy function extrema on the position of the object before the motion and on the direction of motion is studied. When moving in an arbitrary direction, different types of extrema are observed: point and extended. At vertical or horizontal movements, the energy functions graphs have only two-dimensional pulses. Additional criteria for eliminating ambiguities are proposed. The ambiguities are due to the fact that the number and nature of extrema may coincide for different directions of motion and location of objects in different quadrants of the coordinate plane. The extrema of energy functions for object movements in all possible directions and locations in any quadrant of a video sequence frame are determined. The obtained results showed that energy functions for object motion in arbitrary direction have a large number of characteristic regions containing point and extended extrema. The configuration of extrema depends both on the direction of motion and on the quadrant of the image in which the object is located. Therefore, it is most advisable to begin to determine the spatial coordinates of an object and the magnitude of its horizontal/vertical movement by finding the maximum and minimum boundaries of energy functions, and then refine it using the coordinates of the extrema. Consideration of characteristic regions and extrema of scalar fields of energy functions can be used in the development of algorithms effective in terms of "accuracy/speed" ratio, and can increase the performance of image and video processing systems. Algorithms for determining the object position, velocity and direction of its movement by analyzing scalar fields of energy spectra components can be used as an additional or main tool for motion identification in technical vision systems.

Low-Cost CCTV for Home Security With Face Detection Base on IoT

Monitoring is a necessary part of Home surveillance that can be done through the internet network as a security measure. Many CCTV cameras on the market today continue to employ analog and conventional technology, specifically coaxial wire. As a result, extra expenditures for CCTV system wiring are required; besides being more expensive, the installation takes more handling, as the picture data cable and control signal cable cannot be merged. This project aims to develop a security system capable of detecting object movement in real-time utilizing a webcam camera attached to a raspberry pi. The findings of this study enable the development of a low-cost CCTV system that can be monitored remotely via the Internet of Things.

Ufuk Amaçlı Genetik Programlama ile Hava Durumu Tahminine Güven Aralıklı Yaklaşım

Being able to forecast events has always been important for humans. Humans did forecasting by inspecting movements of material and non-material objects in ancient times. However, thanks to the technological developments and the increasing amount of data in recent years, forecasting is now done by computers, especially by machine learning methods. One of the areas where these methods are used frequently is numerical weather forecasting. In this type of forecast, short, medium and long-term weather forecasts are made using historical data. However, predictions are inherently error-prone phenomena and should be stated which error range the predictions fall. In this study, numerical weather forecasting was done by combining Genetic Programming and Inductive Conformal Prediction method. The effect of 10 and 20 days of historical data on short (1-day), medium (3-days) and long-term (5-days) weather forecasts was examined. Results suggested that Genetic Programming has a good potential to be used in this area. However, when Genetic Programming was combined with the Inductive Conformal Prediction method, it was shown that forecasts gave meaningful results only in short-term; forecasts made for medium and long-term did not produce meaningful results.

The third way: object reordering as ambiguous labeling resolution

Abstract This paper examines free object order alternation in ditransitives, focusing on Slovenian. It is shown that neither a scrambling nor a base generation analysis is fully satisfactory. A new analysis based on the Labeling Algorithm is proposed, where it is argued that the introduction of a second object creates an ambiguous labeling scenario ({NP,VP}), which has two equivalent resolutions: (i) movement of the VP with the first object inside, or (ii) movement of the second object. This is shown to derive both free object order in the general case and the restrictions on object order in select contexts, as due to the specifics of the VP-movement analysis it is possible for selectional restrictions to filter out either derivation (i) or (ii).

CNB Net: A Two-Stage Approach for Effective Image Deblurring

Image blur, often caused by camera shake and object movement, poses a significant challenge in computer vision. Image deblurring strives to restore clarity to these images. Traditional single-stage methods, while effective in detail enhancement, often neglect global context in favor of local information. Yet, both aspects are crucial, especially in real-life scenarios where images are typically large and subject to various blurs. Addressing this, we introduce CNB Net, an innovative deblurring network adept at integrating global and local insights for enhanced image restoration. The network operates in two stages, utilizing our specially designed Convolution and Normalization-Based Block (CNB Block) and Convolution and Normalization-Based Plus Block (CNBP Block) for multi-scale information extraction. A progressive learning approach is adopted with a Feature Active Selection (FAS) module at the end of each stage that captures spatial detail information under the guidance of real images. The Two-Stage Feature Fusion (TSFF) module reduces information loss caused by downsampling operations while enriching features across stages for increased robustness. We conduct experiments on the GoPro dataset and the HIDE dataset. On the GoPro dataset, our Peak Signal-to-Noise Ratio (PSNR) result is 32.21 and the Structural Similarity (SSIM) result is 0.950; and on the HIDE dataset, our PSNR result is 30.38 and the SSIM result is 0.932. Our results exceed other similar algorithms. By comparing the generated feature maps, we find that our model takes into account both global and local information well.

Video Classification of Cloth Simulations: Deep Learning and Position-Based Dynamics for Stiffness Prediction.

In virtual reality, augmented reality, or animation, the goal is to represent the movement of deformable objects in the real world as similar as possible in the virtual world. Therefore, this paper proposed a method to automatically extract cloth stiffness values from video scenes, and then they are applied as material properties for virtual cloth simulation. We propose the use of deep learning (DL) models to tackle this issue. The Transformer model, in combination with pre-trained architectures like DenseNet121, ResNet50, VGG16, and VGG19, stands as a leading choice for video classification tasks. Position-Based Dynamics (PBD) is a computational framework widely used in computer graphics and physics-based simulations for deformable entities, notably cloth. It provides an inherently stable and efficient way to replicate complex dynamic behaviors, such as folding, stretching, and collision interactions. Our proposed model characterizes virtual cloth based on softness-to-stiffness labels and accurately categorizes videos using this labeling. The cloth movement dataset utilized in this research is derived from a meticulously designed stiffness-oriented cloth simulation. Our experimental assessment encompasses an extensive dataset of 3840 videos, contributing to a multi-label video classification dataset. Our results demonstrate that our proposed model achieves an impressive average accuracy of 99.50%. These accuracies significantly outperform alternative models such as RNN, GRU, LSTM, and Transformer.

Object detection and tracking using TSM-EFFICIENTDET and JS-KM in adverse weather conditions

An efficient model to detect and track the objects in adverse weather is proposed using Tanh Softmax (TSM) EfficientDet and Jaccard Similarity based Kuhn-Munkres (JS-KM) with Pearson-Retinex in this paper. The noises were initially removed using Differential Log Energy Entropy adapted Wiener Filter (DLE-WF). The Log Energy Entropy value was calculated between the pixels instead of calculating the local mean of a pixel in the normal Wiener filter. Also, the segmentation technique was carried out using Fringe Binarization adapted K-Means Algorithm (FBKMA). The movement of segmented objects was detected using the optical flow technique, in which the optical flow was computed using the Horn-Schunck algorithm. After motion estimation, the final step in the proposed system is object tracking. The motion-estimated objects were treated as the target that is initially in the first frame. The target was tracked by JS-KM algorithm in the subsequent frame. At last, the experiential evaluation is conducted to confirm the proposed model’s efficacy. The outcomes of Detection in Adverse Weather Nature (DAWN) dataset proved that in comparison to the prevailing models, a better performance was achieved by the proposed methodology.

Tissue ultrasound imaging based on wavelet correlation analysis and pulse-inversion technique.

Pulse-inversion-based tissue harmonic imaging has been utilized for many years because it can effectively eliminate the harmonic leakage and produce low side-lobe. However, the pulse inversion method is sensitive to imaging object movements, which may result in motion artifacts. Spatial resolution and contrast were limited. To improve ultrasound image quality by a new pulse-inversion-based tissue harmonic imaging technique. Continuous wavelet transform is applied to investigate the correlation between mother wavelet and the received echoes from two opposite pulses. To get a better correlation, a novel mother wavelet named 'tissue wavelet' is designed based on the Khokhlov-Zabolotskaya- Kuznetsov (KZK) wave equation. Radio frequency data were obtained from open Ultrasonix SonixTouch imaging system. Experiments were carried on ultrasonic tissue phantom, human carotid artery and human liver. The average improvement of lateral spatial resolution is 49.52% compared to pulse-inversion-based tissue second-harmonic Imaging (PIHI). Contrast ratio (CR) and contrast-to-noise ratio (CNR) increased by 5.55 dB and 1.40 dB over PIHI. Tissue wavelet performs better than Mexh and Morl wavelet in lateral spatial resolution, CR, and CNR. The proposed technique effectively improves the imaging quality in lateral spatial resolution, CR, and CNR.

Планирование движения мобильных объектов на основе модификации метода вероятностной дорожной карты

The article formalizes the task of planning the trajectory of movement of mobile objects, and the minimum length of the trajectory is chosen as a criterion for the optimality of movement. When solving the problem, preference is given to the method of probabilistic route networks and modifications of this method are proposed. The study of the effectiveness of modified algorithms of probabilistic route networks is carried out using examples of solving the problem of automation and optimization of planning the trajectory of a mobile object in an external environment with restrictions on the size of the object. The developed software of the modified algorithms is illustrated by program listings and trajectories of movement of mobile objects.