Motion Object Research Articles

Video analysis to examine Kepler’s laws of planetary motion

Video motion analysis allows tracing trajectories of objects in motion and is an established method in physics education. Tablet computers, with their integrated cameras, offer the opportunity to both record and analyze dynamic motions during experiments on a single device. This enables students to work without transitioning the data between camera and computer, but with ubiquitously available applications on mobile devices. This article describes a model experiment to examine Kepler’s laws of motion using tablet computers with a video analysis application.

An Edge-device Based Fast Fall Detection Using Spatio-temporal Optical Flow Model.

The elderly fall detection is one critical function in health of the elderly. A real-time fall detection for the elderly has been a significant healthcare issue. The traditional video analysis on cloud has large communication overhead. In this paper, a fast fall detection system based on the spatio-temporal optical flow model is proposed, which is further deeply compressed by a structured tensorization towards an implementation on edge devices. Firstly, an object extractor is built to extract motion objects from video clips. The spatio-temporal optical flow model is formed to estimate optical flow fields of motion objects. It can extract features from objects and their corresponding optical flow fields. Then these two features are fused to form new spatio-temporal features. Finally, the tensor-compressed model processes the fused features to determine fall detection, where the strongest optical field would indicate the fall. We conduct experiments with Multicam and URFD datasets.Clinical relevance- It demonstrates that the proposed model achieves the accuracy of 96.23% and 99.37%, respectively. Besides, it attains the inference speed of 83.3 FPS and storage reduction of 210.9×. Our work is further implemented on an AI acceleration core based edge device, and the runtime is reduced by 9.21×.This high performance system can be applied to the field of clinical monitoring in the future.

Object Tracking in Video Using the TLD and CMT Fusion Model

Object tracking has been an attractive study topic in computer vision in recent years, thanks to the development of video monitoring systems. Tracking-Learning Detection (TLD), Compressive Tracking (CT), and Clustering of Static-Adaptive Correspondences for Deformable Object Tracking are some of the state-of-the-art methods for motion object tracking (CMT). We present a fusion model that combines TLD and CMT in this study. To restrict the calculation time of the CMT technique, the fusion TLD CMT model enhanced the TLD benefits of computation time and accuracy on t no deformable objects. The experimental results on the Vojir dataset for three techniques (TLD, CMT, and TLD CMT) demonstrated that our fusion proposal successfully trades off CMT accuracy for computing time.

An effective motion object detection using adaptive background modeling mechanism in video surveillance system

Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

“It Should Be on Every Surgeon’s Table”: The Reception and Adoption of Joseph Maclise’s Surgical Anatomy (1851) in the United States

This article is part of the Objects in Motion series in British Art Studies, which is funded by the Terra Foundation for American Art. Projects in the series examine cross-cultural dialogues between Britain and the United States, and may focus on any aspect of visual and material culture produced before 1980. The aim of Objects in Motion is to explore the physical and material circumstances by which art is transmitted, displaced, and recontextualised, as well as the transatlantic processes that create new markets, audiences, and meanings.

Optimizing Build Orientation for Support Removal using Multi-Axis Machining

Parts fabricated by additive manufacturing (AM) are often fabricated first as a near-net shape, a combination of intended nominal geometry and sacrificial support structures, which need to be removed in a subsequent post-processing stage using subtractive manufacturing (SM). In this paper, we present a framework for optimizing the build orientation with respect to removability of support structures. In particular, given a general multi-axis machining setup and sampled build orientations, we define a Pareto-optimality criterion based on the total support volume and the “secluded” support volume defined as the support volume that is not accessible by a given set of machining tools. Since total support volume mainly depends on the build orientation and the secluded volume is dictated by the machining setup, in many cases the two objectives are competing and their trade-off needs to be taken into account. The accessibility analysis relies on the inaccessibility measure field (IMF), which is a continuous field in the Euclidean space that quantifies the inaccessibility of each point given a collection of tools and fixturing devices. The value of IMF at each point indicates the minimum possible volumetric collision between objects in relative motion including the part, fixtures, and the tools, over all possible tool orientations and sharp points on the tool. We also propose an automated support removal planning algorithm based on IMF, where a sequence of actions are provided in terms of the fixturing devices, cutting tools, and tool orientation at each step. In our approach, each step is chosen based on the maximal removable volume to iteratively remove accessible supports. The effectiveness of the proposed approach is demonstrated through benchmark examples in 2D and realistic examples in 3D.

Dissipation Mechanisms for Fluids and Objects in Relative Motion Described by the Navier-Stokes Equation.

This work demonstrates that an additional resistance term should be included in the Navier–Stokes equation when fluids and objects are in relative motion. This is based on an observation that the effect of the microscopic molecular random velocity component parallel to the macroscopic flow direction is neglected. The two components of the random velocity perpendicular to the local mean flow direction are accounted for by the viscous resistance, e.g., by Stokes’ law for spherical objects. The relationship between the mean- and the random velocity in the longitudinal direction induces differences in molecular collision velocities and collision frequency rates on the up- and downstream surface areas of the object. This asymmetry therefore induces flow resistance and energy dissipation. The flow resistance resulting from the longitudinal momentum transfer mode is referred to as thermal resistance and is quantified by calculating the net difference in pressure up- and downstream the surface areas of a sphere using a particle velocity distribution that obeys Boltzmann’s transport equation. It depends on the relative velocity between the fluid and the object, the number density and the molecular fluctuation statistics of the fluid, and the area of the object and the square root of the absolute temperature. Results show that thermal resistance is dominant compared to viscous resistance considering water and air in slow relative motion to spherical objects larger than nanometer-size at ambient temperature and pressure conditions. Including the thermal resistance term in the conventional expression for the terminal velocity of spherical objects falling through liquids, the Stokes–Einstein relationship and Darcy’s law, corroborates its presence, as modified versions of these equations fit observed data much more closely than the conventional expressions. The thermal resistance term can alternatively resolve d’Alembert’s paradox as a finite flow resistance is predicted at both low and high relative fluid–object velocities in the limit of vanishing fluid viscosity.

The Optimization Model of Composite Material UAV Shooting Flight Routes

UAV is one of the important achievements of scientific and technological innovation, which has been widely used in various fields of life in recent years. UAVs of composite materials are the most popular because the composite itself has designability, and can be optimized according to the strength and stiffness of the aircraft without changing the structural weight. In order to ensure the UAV to carry out more accurate image acquisition under the actual ground conditions, through the analysis of decision variables, this paper uses the linear weighting of image shooting range and pixel accuracy to establish the objective function, and establishes the optimal shooting angle under different constraints Considering the actual motion characteristics of objects such as automobiles, this paper assumes that the tracked ground targets are a class of motion objects with non-integrity constraints when the flight altitude of UAV is fixed and the flight speed is constant. The ground target model and the flight dynamics nonlinear model are established. This paper presents a new RRT* algorithm for path planning based on the obstacles encountered by UAV during flight, that is, using the cost function to select the node with the minimum cost in the field of expanding nodes as the parent node, using the MATLAB to select a reasonable obstacle avoidance strategy, the global optimal route is obtained by smoothing processing, and the data simulation test is carried out. The experimental results show that the model is robust.

Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data

Recently we observe a significant increase in the amount of easily accessible data on transport and mobility. This data is mostly massive streams of high velocity, magnitude, and heterogeneity, which represent a flow of goods, shipments and the movements of fleet. It is therefore necessary to develop a scalable framework and apply tools capable of handling these streams. In the paper we propose an approach for the selection of software for stream processing solutions that may be used in the transportation domain. We provide an overview of potential stream processing technologies, followed by the method for choosing the selected software for real-time analysis of data streams coming from objects in motion. We have selected two solutions: Apache Spark Streaming and Apache Flink, and benchmarked them on a real-world task. We identified the caveats and challenges when it comes to implementation of the solution in practice.

An Efficient Velocity Estimation Approach for Face Liveness Detection using Sparse Optical Flow Technique

Objectives: To propose a new liveness detection algorithm using optical flow to ensure the presence of actual live face into a photograph or 2D masks in face recognition biometric security systems. Methods: This work proposes an anti-spoofing model namely Sparse Optical Flow Technique with Velocity Estimation Approach (SOFT_VEA). Optical flow is an effective method for tracking objects in motion. It is adapted in this work to capture facial movements and decide the liveness state. The proposed algorithm considers real faces and two kinds of photo imposters. Findings: From the input video, the motion information of specific facial landmark points is captured by an optical flow algorithm. Then, the velocity of those landmark points is estimated via Euclidean distance. Based on this calculated velocity, the fake face is discriminated from the real face using a threshold value. The Empirical study shows that the proposed face liveness detection model is effective with an accuracy of 88% and Half Total Error Rate (HTER) of 2.45. Novelty: The proposed work is based on real face and photo imposters. The liveness detection algorithm is developed with a novel velocity estimation approach. It is very helpful for biometric security systems. Keywords Biometric security system, Liveness detection, Anti­spoofing, Facial landmarks, Optical flow, Euclidean distance

A unified psychological space for human perception of physical and social events

One of the great feats of human perception is the generation of quick impressions of both physical and social events based on sparse displays of motion trajectories. Here we aim to provide a unified theory that captures the interconnections between perception of physical and social events. A simulation-based approach is used to generate a variety of animations depicting rich behavioral patterns. Human experiments used these animations to reveal that perception of dynamic stimuli undergoes a gradual transition from physical to social events. A learning-based computational framework is proposed to account for human judgments. The model learns to identify latent forces by inferring a family of potential functions capturing physical laws, and value functions describing the goals of agents. The model projects new animations into a sociophysical space with two psychological dimensions: an intuitive sense of whether physical laws are violated, and an impression of whether an agent possesses intentions to perform goal-directed actions. This derived sociophysical space predicts a meaningful partition between physical and social events, as well as a gradual transition from physical to social perception. The space also predicts human judgments of whether individual objects are lifeless objects in motion, or human agents performing goal-directed actions. These results demonstrate that a theoretical unification based on physical potential functions and goal-related values can account for the human ability to form an immediate impression of physical and social events. This ability provides an important pathway from perception to higher cognition.

Faint objects in motion: the new frontier of high precision astrometry

Sky survey telescopes and powerful targeted telescopes play complementary roles in astronomy. In order to investigate the nature and characteristics of the motions of very faint objects, a flexibly-pointed instrument capable of high astrometric accuracy is an ideal complement to current astrometric surveys and a unique tool for precision astrophysics. Such a space-based mission will push the frontier of precision astrometry from evidence of Earth-mass habitable worlds around the nearest stars, to distant Milky Way objects, and out to the Local Group of galaxies. As we enter the era of the James Webb Space Telescope and the new ground-based, adaptive-optics-enabled giant telescopes, by obtaining these high precision measurements on key objects that Gaia could not reach, a mission that focuses on high precision astrometry science can consolidate our theoretical understanding of the local Universe, enable extrapolation of physical processes to remote redshifts, and derive a much more consistent picture of cosmological evolution and the likely fate of our cosmos. Already several missions have been proposed to address the science case of faint objects in motion using high precision astrometry missions: NEAT proposed for the ESA M3 opportunity, micro-NEAT for the S1 opportunity, and Theia for the M4 and M5 opportunities. Additional new mission configurations adapted with technological innovations could be envisioned to pursue accurate measurements of these extremely small motions. The goal of this White Paper is to address the fundamental science questions that are at stake when we focus on the motions of faint sky objects and to briefly review instrumentation and mission profiles.

Face Mask Detection using Deep Learning to Manage Pandemic Guidelines

The field of Computer Vision is a branch of science of the computers and systems of software in which one can visualize and as well as comprehend the images and scenes given in the input. This field is consisting of numerous aspects for example image recognition, the detection of objects, generation of images, image super-resolution and more others. Object detection is broadly utilized for the detection of faces, the detection of vehicles, counting of pedestrians on a certain street, images displayed on the web, security systems and cars with the feature of self-driving. This process also encompasses the precision of every technique for recognizing the objects. The detection of objects is a crucial task; however, it is also a very challenging vision task. It is an analytical subdivide of various applications such as searching of images, image auto-annotation or scene understanding and tracking of various objects. The tracking of objects in motion of video image sequence was one of the most important subjects in computer vision.

Object Visual Detection for Intelligent Vehicles

Traffic scene perception (TSP) aims to extract accurate real-time on-road environment information, which involves three phases detection of objects of interest, recognition of detected objects, and tracking of objects in motion. In this project, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object sub categorization method as a means of capturing the intra class variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection.

Bird expertise does not increase motion sensitivity to bird flight motion.

While motion information is important for the early stages of vision, it also contributes to later stages of object recognition. For example, human observers can detect the presence of a human, judge its actions, and judge its gender and identity simply based on motion cues conveyed in a point-light display. Here we examined whether object expertise enhances the observer's sensitivity to its characteristic movement. Bird experts and novices were shown point-light displays of upright and inverted birds in flight, or upright and inverted human walkers, and asked to discriminate them from spatially scrambled point-light displays of the same stimuli. While the spatially scrambled stimuli retained the local motion of each dot of the moving objects, it disrupted the global percept of the object in motion. To estimate a detection threshold in each object domain, we systematically varied the number of noise dots in which the stimuli were embedded using an adaptive staircase approach. Contrary to our predictions, the experts did not show disproportionately higher sensitivity to bird motion, and both groups showed no inversion cost. However, consistent with previous work showing a robust inversion effect for human motion, both groups were more sensitive to upright human walkers than their inverted counterparts. Thus, the result suggests that real-world experience in the bird domain has little to no influence on the sensitivity to bird motion and that birds do not show the typical inversion effect seen with humans and other terrestrial movement.

What Is Moroccan Literature? History of an Object in Motion

Abstract What is Moroccan literature, where and when does it happen, and in what languages? In this essay, we tackle these questions by tracing the evolution of the definition of “Moroccan literature” from the first half of the twentieth century until the present. The earliest works of Moroccan literary historiography, such as ʿAbd Allah Kannūn’s al-Nubūgh al-maghribī fī al-adab al-ʿarabī (1937), situated Moroccan literature within the Arabic literary tradition and treated Moroccan literature as an important element in the “Arab-Islamic” identity promoted by the Moroccan nationalist movement. Since Moroccan independence in 1956, this definition of Moroccan literature has come under increasing pressure, as the languages and imaginative geographies of Moroccan literature have expanded to include new voices. In what follows, we consider these debates through a survey of a diverse corpus of literary-historical works that throw into question the linguistic, temporal, and spatial borders of Moroccan literature (and of Morocco itself).

Moving object properties-based video saliency detection

Video saliency detection, which suffers from the interference of complicated motion and complex background, aims at discovering the motion-related and the most noticeable object in a video sequence while maintaining the spatiotemporal consistency of saliency maps. We propose a video saliency detection approach by constructing the moving object properties, meaning that motion is in concurrence with a general object for detected regions. With the assistance of different key frame strategies, the consistency propagation, which is respectively conducted to refine the obtained results of motion saliency and object proposals to improve low-quality detections and avoid the excessive accumulation of errors, is orderly implemented through sparse reconstruction based on the constructed adjacent relationships. Meanwhile, by integrating the refined object proposals with the refined salient motion detection, a pixel-based fusion strategy is applied to locate the most trustworthy motion object regions and suppress detection noises, such as dynamic background and stationary objects. Moreover, a Bayesian fusion framework that incorporates the global features obtained using low-rank constraints is employed to further enhance the accuracy and global temporal consistency for the obtained initial motion object regions. Then based on the obtained priors of motion objects and background regions, the spatial saliency map is estimated using the geodesic distances transform to discover more complete and spatially salient motion objects. Finally, an energy optimization function is proposed to intuitively integrate multiple saliency clues (i.e., spatial saliency, global features, and motion objects) and generate the global spatiotemporal consistency saliency map. Experimental results on three different benchmark datasets demonstrate that the proposed method successfully infers video moving objects and extracts the most salient regions, outperforming the state-of-the-art methods.

Geese, Ganders, and Sir Isaac Newton: Examining the Potential Impact of DHS v. Regents and Department of Commerce v. New York on Rescissions of Agency Actions

Agency actions, like objects in motion, resist being reversed. That resistance is administrative inertia and is measured by the amount of effort an administration must expend to reverse it in compliance with the APA. The principle of stare decisis implies that the amount of effort that one administration must expend to reverse a predecessor’s action ought to be constant across administrations. DHS v. Regents and Dep't of Commerce v. New York, however, added mass to certain regulatory actions by requiring that, before an agency may rescind an action that confers a benefit, the agency must separately consider each part of a policy, articulate granular and specific decisions for rescission, consider reliance interests and alternatives, and ensure that the rationale is directed at a concrete outcome and that the record shows it. This article sets about identifying those agency actions that have had their administrative mass increased and measuring just how much more force is now needed to reverse them. It then applies those observations to the Biden Administration’s aggressive efforts to rescind various Trump-administration actions. The article concludes that if the courts apply these two decisions to the Biden Administration in the same way that the Supreme Court applied them to the Trump Administration, the Biden Administration will likely find it much harder to rescind many Trump-Administration policies than it was for past presidents to rescind the policies of their predecessors. This increased administrative inertia will be most noticeable in agency actions that loosened environmental restrictions and relaxed regulatory burdens on commerce.

Eulerian derivation of the conservation equation for energy in a non-inertial frame of reference in arbitrary motion

The standard inertial Navier–Stokes equations consisting of the conservation equations for mass, momentum and energy are often used to investigate the motion of a compressible fluid around an object that is in arbitrary motion. The non-inertial form of the Navier–Stokes equations can be used to accurately capture the acceleration effects that arise from the unsteady motion. The acceleration source terms that arise in the conservation equation for momentum have been extensively documented. In this paper, an Eulerian approach for deriving the apparent forces is presented to transform the governing conservation equation for energy into a non-inertial reference frame that is in arbitrary motion. The Eulerian approach is based on successive Galilean transformations between an inertial frame, an orientation-preserving non-inertial frame and a rotating non-inertial frame. The paper demonstrates that for an object in arbitrary motion, the rate of work done due to fictitious forces affects the rate of change of the total energy. The fictitious work arises in the kinetic energy equation while the internal energy and enthalpy equations remain invariant in the non-inertial frame. The present derivation is a step towards quantifying the contribution of the fictitious work terms to the heat transfer of a body that is accelerating/decelerating.

Objectscapes: a manifesto for investigating the impacts of object flows on past societies

World history is often framed in terms of flows of people: humans coming ‘out of Africa’, the spread of farmers in the Holocene, the disruptions of the ‘Sea Peoples’, or ‘colonisation’ by Phoenicians, Greeks and Romans. In this article, the authors argue that world history is also about the flows of objects. To illuminate the impacts of objects on past societies, they introduce the concept of ‘objectscapes’ as a means of writing new kinds of histories of human-thing entanglements, in which objects in motion have roles to play—beyond representation—over both the short and long term. To illustrate, they present examples from two regions at the end of the first millennium BC: southern Germany and northern Syria.