Local Motion Analysis Research Articles

Physical Violence Detection System to Prevent Student Mental Health Disorders Based on Deep Learning

Physical violence in the educational environment by students often occurs and leads to criminal acts. Apart from that, repeated acts of physical violence can be considered non-verbal bullying. This bullying can hurt the victim, causing physical disorders, mental health, impaired social relationships and decreased academic performance. However, monitoring activities against acts of violence currently being carried out have weaknesses, namely weak supervision by the school. A deep Learning-based physical violence detection system, namely LSTM Network, is the solution to this problem. In this research, we develop a Convolutional Neural Network to detect acts of violence. Convolutional Neural Network extracts features at the frame level from videos. At the frame level, the feature uses long short-term memory in the convolutional gate. Convolutional Neural Networks and convolutional short-term memory can capture local spatio-temporal features, enabling local video motion analysis. The performance of the proposed feature extraction pipeline is evaluated on standard benchmark datasets in terms of recognition accuracy. A comparison of the results obtained with state-of-the-art techniques reveals the promising capabilities of the proposed method for recognising violent videos. The model that has been trained and tested will be integrated into a violence detection system, which can provide ease and speed in detecting acts of violence that occur in the school environment.

Fractal Analysis of Local Activity and Chaotic Motion in Nonlinear Nonplanar Vibrations for Cantilever Beams

Many problems in practical engineering can be simplified as the cantilever beam model, which is generally studied by theoretical analysis, experiment, and numerical simulation. This paper discusses the local activity of the nonlinear nonplanar motion of a cantilever beam at the equilibrium point. Firstly, the equilibrium point of the model and the Jacobian matrix have been calculated. The stability of the characteristic root corresponding to the characteristic polynomial has been analyzed. Secondly, the corresponding complexity function of the model at the equilibrium point has been given. Then, the local activity region of the model at the equilibrium point can be obtained by using the theory of the local activity. Based on the actual engineering research background, the damping coefficient is generally taken as 0 < c < 1. The cantilever beam model is the local activity at the equilibrium point only if the parameters of the model satisfy a certain condition. In the numerical simulation, it is found that when the proper parameters are selected in the local activity region, the cantilever beam can exhibit different types of chaotic motion. The local activity theory provides a theoretical basis for the parameter selection of the chaotic motion in the cantilever beam.

RETRACTED: Dynamic image sampling and swimming motion image recognition in immersive virtual reality

Video Analysis Underwater Virtual Reality Correction, common in technical analysis, supports elite swimming coaches and athletes as a visual aid. The underwater visual environment and dynamic camera behavior can degrade the video, which is considered an undesirable challenge. The athlete's movement can also blur the field of vision and create a cavitation effect on the air bubbles. Detects problems with existing systems configured using multiple image processing algorithms. The Naive Bayes algorithm studied swimming motion analysis and applied a video of broadcast sports to recognize immersive virtual reality by swimming and proposed here. The process is more sensitive to noise than global motion recovery, 1) local motion is usually buried in a complex motion, 2) clutter containing multiple objects: local motion analysis is for two reasons. It's difficult. However, a useful method for local motion analysis is to understand significant human activity from image sequences. In this study, Naive Bayes uses third robust motion estimation and prominent colors to sample detected objects and extract swimming motion. Swimmer From the moving image based on the above view, the observed wave resistance is observed through the suspension camera. The recorder data is synchronized with the immersive virtual reality of evaluating the actual moving image to capture detailed swimming movements. Principal Component Analysis (PCA) is analyzed by time-frequency. These recorders' data are displayed in chronological order with a different swimming immersion virtual reality than the main frequency.

Crowd flow estimation from calibrated cameras

Many crowd analysis methods rely on optical flow techniques to estimate the main moving directions. In this work, we propose a crowd flow filtering approach for calibrated cameras that can be coupled to any generic optical flow method. It projects the input optical flow to the world coordinate system, performs a local motion analysis exploring a Social Forces Model and then projects the filtered flow back onto the image plane. The method was tested on publicly available datasets involving crowded scenarios used in conjunction with different optical flow methods, and results indicate that the proposed filtering method provides coherent crowd flows when coupled to the tested methods.

Robust background identification for dynamic video editing

Extracting background features for estimating the camera path is a key step in many video editing and enhancement applications. Existing approaches often fail on highly dynamic videos that are shot by moving cameras and contain severe foreground occlusion. Based on existing theories, we present a new, practical method that can reliably identify background features in complex video, leading to accurate camera path estimation and background layering. Our approach contains a local motion analysis step and a global optimization step. We first divide the input video into overlapping temporal windows, and extract local motion clusters in each window. We form a directed graph from these local clusters, and identify background ones by finding a minimal path through the graph using optimization. We show that our method significantly outperforms other alternatives, and can be directly used to improve common video editing applications such as stabilization, compositing and background reconstruction.

Local collective motion analysis for multi-probe dynamic imaging and microrheology

Dynamical artifacts, such as mechanical drift, advection, and hydrodynamic flow, can adversely affect multi-probe dynamic imaging and passive particle-tracking microrheology experiments. Alternatively, active driving by molecular motors can cause interesting non-Brownian motion of probes in local regions. Existing drift-correction techniques, which require large ensembles of probes or fast temporal sampling, are inadequate for handling complex spatio-temporal drifts and non-Brownian motion of localized domains containing relatively few probes. Here, we report an analytical method based on local collective motion (LCM) analysis of as few as two probes for detecting the presence of non-Brownian motion and for accurately eliminating it to reveal the underlying Brownian motion. By calculating an ensemble-average, time-dependent, LCM mean square displacement (MSD) of two or more localized probes and comparing this MSD to constituent single-probe MSDs, we can identify temporal regimes during which either thermal or athermal motion dominates. Single-probe motion, when referenced relative to the moving frame attached to the multi-probe LCM trajectory, provides a true Brownian MSD after scaling by an appropriate correction factor that depends on the number of probes used in LCM analysis. We show that LCM analysis can be used to correct many different dynamical artifacts, including spatially varying drifts, gradient flows, cell motion, time-dependent drift, and temporally varying oscillatory advection, thereby offering a significant improvement over existing approaches.

Sub-micron strain analysis of local stick-slip motion of individual shear bands in a bulk metallic glass

Nanodot deposition on a side surface of a rectangular sample and digital image correlation are used to quantify the in-plane strain fields associated with the propagation of a shear band in a PdNiP bulk metallic glass, induced by rolling. Within the resolution of the method related to an average inter-dot distance of 100 nm, deformation is found to be highly localized at the shear bands, while alternating areas with a size of 100–400 nm with opposite local shear strains are found. This phenomenon substantiates a local stick-slip nature of shear band propagation during the metallic glass deformation, even during rolling.

Physical and Biological Constraints Govern Perceived Animacy of Scrambled Human Forms

Point-light animations of biological motion are perceived quickly and spontaneously, giving rise to an irresistible sensation of animacy. However, the mechanisms that support judgments of animacy based on biological motion remain unclear. The current study demonstrates that animacy ratings increase when a spatially scrambled animation of human walking maintains consistency with two fundamental constraints: the direction of gravity and congruency between the directions of intrinsic and extrinsic motion. Furthermore, using a reverse-correlation method, we show that observers employ structural templates, or form-based “priors,” reflecting the prototypical mammalian body plan when attributing animacy to scrambled human forms. These findings reveal that perception of animacy in scrambled biological motion involves not only analysis of local intrinsic motion, but also its congruency with global extrinsic motion and global spatial structure. Thus, they suggest a strong influence of prior knowledge about characteristic features of creatures in the natural environment.

Processing of visual signals related to self-motion in the cerebellum of pigeons.

In this paper I describe the key features of optic flow processing in pigeons. Optic flow is the visual motion that occurs across the entire retina as a result of self-motion and is processed by subcortical visual pathways that project to the cerebellum. These pathways originate in two retinal-recipient nuclei, the nucleus of the basal optic root (nBOR) and the nucleus lentiformis mesencephali, which project to the vestibulocerebellum (VbC) (folia IXcd and X), directly as mossy fibers, and indirectly as climbing fibers from the inferior olive. Optic flow information is integrated with vestibular input in the VbC. There is a clear separation of function in the VbC: Purkinje cells in the flocculus process optic flow resulting from self-rotation, whereas Purkinje cells in the uvula/nodulus process optic flow resulting from self-translation. Furthermore, Purkinje cells with particular optic flow preferences are organized topographically into parasagittal “zones.” These zones are correlated with expression of the isoenzyme aldolase C, also known as zebrin II (ZII). ZII expression is heterogeneous such that there are parasagittal stripes of Purkinje cells that have high expression (ZII+) alternating with stripes of Purkinje cells with low expression (ZII−). A functional zone spans a ZII± stripe pair. That is, each zone that contains Purkinje cells responsive to a particular pattern of optic flow is subdivided into a strip containing ZII+ Purkinje cells and a strip containing ZII− Purkinje cells. Additionally, there is optic flow input to folia VI–VIII of the cerebellum from lentiformis mesencephali. These folia also receive visual input from the tectofugal system via pontine nuclei. As the tectofugal system is involved in the analysis of local motion, there is integration of optic flow and local motion information in VI–VIII. This part of the cerebellum may be important for moving through a cluttered environment.

Local Motion Analysis Reveals Impact of the Dynamic Cytoskeleton on Intracellular Subdiffusion

Intracellular transport is a complex interplay of ballistic transport along filaments and of diffusive motion, reliably delivering material and allowing for cell differentiation, migration, and proliferation. The diffusive regime, including subdiffusive, Brownian, and superdiffusive motion, is of particular interest for inferring information about the dynamics of the cytoskeleton morphology during intracellular transport. The influence of dynamic cytoskeletal states on intracellular transport are investigated in Dictyostelium discoideum cells by single particle tracking of fluorescent nanoparticles, to relate quantitative motion parameters and intracellular processes before and after cytoskeletal disruption. A local mean-square displacement (MSD) analysis separates ballistic motion phases, which we exclude here, from diffusive nanoparticle motion. In this study, we focus on intracellular subdiffusion and elucidate lag-time dependence, with particular focus on the impact of cytoskeleton compartments like microtubules and actin filaments. This method proves useful for binary motion state distributions. Experimental results are compared to simulations of a data-driven Langevin model with finite velocity correlations that captures essential statistical features of the local MSD algorithm. Specifically, the values of the mean MSD exponent and effective diffusion coefficients can be traced back to negative correlations of the motion's increments. We clearly identify both microtubules and actin filaments as the cause for intracellular subdiffusion and show that actin-microtubule cross talk exerts viscosifying effects at timescales larger than 0.2 s. Our findings might give insights into material transport and information exchange in living cells, which might facilitate gaining control over cell functions.

Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data

Motivated by radiotherapy of lung cancer non- linear registration is applied to estimate 3D motion fields for local lung motion analysis in thoracic 4D CT images. Reliability of analysis results depends on the registration accuracy. Therefore, our study consists of two parts: optimization and evaluation of a non-linear registration scheme for motion field estimation, followed by a registration-based analysis of lung motion patterns. The study is based on 4D CT data of 17 patients. Different distance measures and force terms for thoracic CT registration are implemented and compared: sum of squared differences versus a force term related to Thirion's demons registration; masked versus unmasked force computation. The most accurate approach is applied to local lung motion analysis. Masked Thirion forces outperform the other force terms. The mean target registration error is 1.3 ± 0.2 mm, which is in the order of voxel size. Based on resulting motion fields and inter-patient normalization of inner lung coordinates and breathing depths a non-linear dependency between inner lung position and corresponding strength of motion is identified. The dependency is observed for all patients without or with only small tumors. Quantitative evaluation of the estimated motion fields indicates high spatial registration accuracy. It allows for reliable registration-based local lung motion analysis. The large amount of information encoded in the motion fields makes it possible to draw detailed conclusions, e.g., to identify the dependency of inner lung localization and motion. Our examinations illustrate the potential of registration-based motion analysis.

Fusing bio-inspired vision data for simplified high level scene interpretation: Application to face motion analysis

This paper proposes to demonstrate the advantages of using certain properties of the human visual system in order to develop a set of fusion algorithms for automatic analysis and interpretation of global and local facial motions. The proposed fusion algorithms rely on information coming from human vision models such as human retina and primary visual cortex previously developed at Gipsa-lab. Starting from a set of low level bio-inspired modules (static and moving contour detector, motion event detector and spectrum analyser) which are very efficient for video data pre-processing, it is shown how to organize them together in order to achieve reliable face motion interpretation. In particular, algorithms for global head motion analysis such as head nods, for local eye motion analysis such as blinking, for local mouth motion analysis such as speech lip motion and yawning and for open/close mouth/eye state detection are proposed and their performances are assessed. Thanks to the use of human vision model pre-processing which decorrelates visual information in a reliable manner, fusion algorithms are simplified and remain robust against traditional video acquisition problems (light changes, object detection failure, etc.).

INDEPENDENT COMPONENTS OF OPTICAL FLOW IN A MULTIRESOLUTION IMAGE SEQUENCE

In this paper, we present an algorithm for the hierarchical recognition of an environment using independent components of optical flow fields for the visual navigation of a mobile robot. For the computation of optical flow, the pyramid transform of an image sequence is used for the analysis of global and local motion. Our algorithm detects the planar region and obstacles in the image from optical flow fields at each layer in the pyramid. Therefore, our algorithm allows us to achieve both global perception and local perception for robot vision. We show experimental results for both test image sequences and real image sequences captured by a mobile robot. Furthermore, we show some aspects of this work from the viewpoint of information theory.

Plaid perception is only subtly impaired in strabismic amblyopia

Amblyopes exhibit a global motion anomaly that implicates processing beyond the local motion analysis of V1 possibly involving areas MT and MST in the extra-striate cortex. Here, we sought to further investigate this deficit by measuring the perception of moving plaid stimuli by amblyopic observers, since there is good physiological evidence that the motion of such stimuli is determined by processes beyond V1. The conditions under which the two moving components constituting the plaids were seen to cohere or move transparently over one another were investigated by manipulating their relative spatial frequencies. Percepts were measured using both short presentation durations, where both the percept and the direction of motion were reported, and long presentation durations where the bi-stability of the stimulus was directly measured. In addition, we measured the ability of amblyopic eyes to perceive globally coherent motion in a multiple aperture stimulus. We found a small increased tendency for both amblyopic and fellow-fixing eyes to perceive short duration plaid stimuli as coherent relative to control eyes, but no difference for long duration plaids. In addition, amblyopic eyes saw less coherence in multiple aperture stimuli than fellow-fixing eyes but were not reliably different from control eyes. We therefore conclude that the neural mechanisms underlying plaid perception are only subtly abnormal in amblyopia.

Perception of biological motion from limited-lifetime stimuli

The visual perception of human movement from sparse point-light walkers is often believed to rely on local motion analysis. We investigated the role of local motion in the perception of human walking, viewed from the side, in different tasks. The motion signal was manipulated by varying point lifetime. We found the task of coherence discrimination, commonly used in biological motion studies, to be inappropriate for testing the role of motion. A task requiring temporal information showed a strong performance drop when fewer points were used or when the image sequence was sampled and displayed at a reduced frame rate. Irrespective of the frame rate, performance did not vary with point lifetime. We concluded that local motion is not required for the perception of tested biological movements, suggesting that the analysis of biological motion does not benefit from examining local motion. The reliance of perception on the number of displayed points and frames supports the idea that biological motion is perceived from a sequence of spatiotemporally sampled forms.

An illusory transformation of optic flow fields without local motion interactions

The focus of expansion (FOE) of a radially expanding optic flow pattern that is overlapped by unidirectional laminar flow is perceptually displaced in the direction of that laminar flow. There is continuing debate on whether this effect is due to local or global motion interactions. Here, we show psychophysically that under conditions without local motion transparency the illusion becomes weaker but can still be observed. In our experiments, the radial and laminar-flow fields were not presented with overlap but separately to the left and right halves of the visual field with a blank vertical strip of 15° horizontal width in between. The illusory shift observed in this condition cannot be explained by local motion interactions because (a) no transparent motion was present in the stimulus, and (b) the receptive fields of cortical cells involved in the analysis of local motion cross the vertical midline of the visual field to a limited extent. We conclude that global motion detectors that integrate motion from both halves of the visual field play a role in shifting the perceived position of the FOE and that local motion interactions may be sufficient, but are not necessary for the optic flow illusion to occur.

Efficient Fourier-Based Approach for Detecting Orientations and Occlusions in Epipolar Plane Images for 3D Scene Modeling

This paper presents a Fourier-based approach for automatically constructing a 3D panoramic model of a natural scene from a video sequence. The video sequences could be captured by an unstabilized camera mounted on a moving platform on a common road surface. As the input of the algorithms, "seamless" panoramic view images (PVIs) and epipolar plane images (EPIs) are generated after image stabilization if the camera is unstabilized. A novel panoramic EPI analysis method is proposed that combines the advantages of both PVIs and EPIs efficiently in three important steps: locus orientation detection in the Fourier frequency domain, motion boundary localization in the spatio-temporal domain, and occlusion/resolution recovery only at motion boundaries. The Fourier energy-based approaches in literature were usually for low-level local motion analysis and are therefore not accurate for 3D reconstruction and are also computationally expensive. Our panoramic EPI analysis approach is both accurate and efficient for 3D reconstruction. Examples of layered panoramic representations for large-scale 3D scenes from real world video sequences are given.

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A Simon effect with stationary moving stimuli.

To clarify whether motion information per se has a separable influence on action control, the authors investigated whether irrelevant direction of motion of stimuli whose overall position was constant over time would affect manual left-right responses (i.e., reveal a motion-based Simon effect). In Experiments 1 and 2, significant Simon effects were obtained for sine-wave gratings moving in a stationary Gaussian window. In Experiment 3, a direction-based Simon effect with random-dot patterns was replicated, except that the perceived direction of motion was based on the displacement of single elements. Experiments 4 and 5 studied motion-based Simon effects to point-light figures that walked in place--displays requiring high-level analysis of global shape and local motion. Motion-based Simon effects occurred when the displays could be interpreted as an upright human walker, showing that a high-level representation of motion direction mediated the effects. Thus, the present study establishes links between high-level motion perception and action.