Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.

An efficient compressed domain moving object segmentation algorithm based on motion vector field

In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing image.In this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM technique.The point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,respectively.Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented.The relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,respectively.Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

MOTION-BASED REGION GROWING SEGMENTATION OF IMAGE SEQUENCES

This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.

Adaptive Motion Segmentation for Changing Background

Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.

Motion feature descriptor based moving objects segmentation

A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search （3DRS） algorithm is used in order to obtain motion information accurately. A motion feature descriptor （MFD） is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm （FCM） is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.

Apply GPCA to Motion Segmentation

In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have confirmed the validity of our method.

Segmental Kinematic Coupling of the Human Spinal Column during Locomotion

As one of the most important daily motor activities, human locomotion has been investigated intensively in recent decades. The locomotor functions and mechanics of human lower limbs have become relatively well understood. However, so far our understanding of the motions and functional contributions of the human spine during locomotion is still very poor and simultaneous in-vivo limb and spinal column motion data are scarce. The objective of this study is to investigate the delicate in-vivo kinematic coupling between different functional regions of the human spinal column during locomotion as a stepping stone to explore the locomotor function of the human spine complex. A novel infrared reflective marker cluster system was constrncted using stereophotogrammetry techniques to record the 3D in-vivo geometric shape of the spinal column and the segmental position and orientation of each functional spinal region simultaneously. Gait measurements of normal walking were conducted. The preliminary results show that the spinal column shape changes periodically in the frontal plane during locomotion. The segmental motions of different spinal functional regions appear to be strongly coupled, indicating some synergistic strategy may be employed by the human spinal column to facilitate locomotion. In contrast to traditional medical imaging-based methods, the proposed technique can be used to investigate the dynamic characteristics of the spinal column, hence providing more insight into the functional biomechanics of the human spine.

Evaluating quality of motion for unsupervised video object segmentation

Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.

Charge Couple Device-Based Systemfor3-di mensional Real Ti me Positioning on the Assessment of Segmental Range of Motion of Lumbar Spine

Objective： To observe the tested results of the segmental range of motion （ROM） of lumbar spine by charge couple device （CCD）-based system for 3-dimensional real-time positioning （CCD system）, and to analyze its clinical significance. Methods： Seven patients with lumbar joint dysfunction and 8 healthy subjects were tested twice by the CCD-based system with an interval of 10 min. Results： The ROM of the patients was obviously lesser than that of the healthy subjects. The measuring data of segmental ROM of lumbar spine by CCD system is correlated significantly to the same data checked later on the same subjects in every direction of the movements. The differences between two checks are usually less than 1 degree. Conclusion： Specially designed CCD based system for 3-dimensional real-time positioning could objectively reflect the segmental ROM of lumbar spine. The system would be of great clinical significance in the assessment of the biomechanical dysfunction of lumbar spine and the effect of the treatment applied.

Seismic behavior of precast segmental column bridges under near-fault forward-directivity ground motions

Precast segmental column bridges exhibit various construction advantages in comparison to traditional monolithic column bridges.However,the lack of cognitions on seismic behaviors has seriously restricted their applications and developments.In this paper,comprehensive investigations are conducted to analyze the dynamic characteristics of precast segmental column bridges under near-fault,forward-directivity ground motions.First,the finite-element models of two comparable bridges with precast segmental columns and monolithic columns are constructed by using OpenSees software,and the nonlinearities of the bridges are considered.Next,three different earthquake loadings are meticulously set up to handle engineering problems,namely recorded near-and far-field ground motions,parameterized pulses,and pulse and residual components extracted from real records.Finally,based on the models and earthquake sets,extensive explorations are carried out.The results show that near-fault forward-directivity ground motions are more threatening than far-field ones;precast segmental column bridges may suffer more pounding impacts than monolithic bridges;the“narrow band”effect caused by near-fault,forward-directivity ground motions may occur in bridges with shorter periods than pulse periods;and pulse and residual components play different roles in seismic responses.

Study on the synthesis of spin labeled poly(styrene-co-maleic acid)s and their segmental motion

Study of the segmental mobility of polymer chains is important when the polymer is used as drag reduction agents of crude oil.Electron spin resonance(ESR)spectroscopy can provide important information about segmental dynamics of polymer chains,which is related to their microenvironment.In this article,we employed an amphiphilic polymer to study the effect of hydrophilic/hydrophobic balance of the polymers on the segmental motion of polymer chains.Poly(styrene-co-maleic acid)(PSMA)was spin labeled with 4-amino TEMPO radicals by increasing the concentration of radical moiety on the polymer chains.The PSMA and spin labeled-PSMAs(SL-PSMAs)were characterized by nuclear magnetic resonance(NMR),Fourier transform infrared(FT-IR)spectroscopy,Cyclic voltammetry(CV)and ESR techniques.Inter-polymer complexes(IPCs)of SL-PSMA-2 were prepared by employing polyethylene glycols(PEGs)of varying molecular weights.The results showed that the increased hydrophobic interactions of nitroxide radicals on the SLPSMAs’chains reduced the rotational mobility of spin labels and the random coil-toglobular transition of polymer chains occurred at higher pH value for SL-PSMAs,which showed a slow motion component in the ESR spectra of SL-PSMAs.Further,by increasing the molecular weight of PEGs in IPCs the complexation was increased,which also reduced the rotational motion of spin labels due to interpolymer hydrogen bonding causing a slow motion component in the ESR spectra.©2021 The Authors.

One Dimensional Random Motion on Segment with Reflecting Edges and Dependent Increments

In previous papers, the author considered the model of anomalous diffusion, defined by stable random process on an interval with reflecting edges. Estimates of the rate convergence of this process distribution to a uniform distribution are constructed. However, recent physical studies require consideration of models of diffusion, defined not only by stable random process with independent increments but multivariate fractional Brownian motion with dependent increments. This task requires the development of special mathematical techniques evaluation of the rate of convergence of the distribution of multivariate Brownian motion in a segment with reflecting boundaries to the limit. In the present work, this technology is developed and a power estimate of the rate of convergence to the limiting uniform distribution is built.

AN AUTOMATIC SEGMENTATION METHOD FOR MOVING OBJECTS BASED ON THE SPATIAL-TEMPORAL INFORMATION OF VIDEO

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.

A Restricted, Adaptive Threshold Segmentation Approach for Processing High-Speed Image Sequences of the Glottis

In this paper, we propose a restricted, adaptive threshold approach for the segmentation of images of the glottis acquired from high speed video-endoscopy (HSV). The approach involves first, identifying a region of interest (ROI) that encloses the vocal-fold motion extent for each image frame as estimated by the different image sequences. This procedure is then followed by threshold segmentation restricted within the identified ROI for each image frame of the original image sequences, or referred to as sub-image sequences. The threshold value is adapted for each sub-image frame and determined by respective minimum gray-scale value that typically corresponds to a spatial location within the glottis. The proposed approach is practical and highly efficient for segmenting a vast amount of image frames since simple threshold method is adapted. Results obtained from the segmentation of representative clinical image sequences are presented to verify the proposed method.

The use of the greater trochanter marker in the thigh segment model:Implications for hip and knee frontal and transverse plane motion

Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marker is important when quantifying frontal and transverse plane hip and knee kinematics,parameters which are particularly relevant to investigate in individuals with conditions such as patellofemoral pain,knee osteoarthritis,anterior cruciate ligament(ACL) injury,and hip pain.The aim of this study was to evaluate the effect of including the greater trochanter in the construction of the thigh segment on hip and knee kinematics during gait.Methods:3D kinematics were collected in 19 healthy subjects during walking using a surface marker system.Hip and knee angles were compared across two thigh segment definitions(with and without greater trochanter) at two time points during stance:peak knee flexion(PKF) and minimum knee flexion(Min KF).Results:Hip and knee angles differed in magnitude and direction in the transverse plane at both time points.In the thigh model with the greater trochanter the hip was more externally rotated than in the thigh model without the greater trochanter(PKF:-9.34°± 5.21° vs.1.40°± 5.22°,Min KF:-5.68°± 4.24° vs.5.01°± 4.86°;p < 0.001).In the thigh model with the greater trochanter,the knee angle was more internally rotated compared to the knee angle calculated using the thigh definition without the greater trochanter(PKF:14.67°± 6.78° vs.4.33°± 4.18°,Min KF:10.54°± 6.71° vs.-0.01°± 2.69°;p < 0.001).Small but significant differences were detected in the sagittal and frontal plane angles at both time points(p < 0.001).Conclusion:Hip and knee kinematics differed across different segment definitions including or excluding the greater trochanter marker,especially in the transverse plane.Therefore when considering whether to include the greater trochanter in the thigh segment model when using a surface markers to calculate 3D kinematics for movement assessment,it is important to have a clear understanding of the effect of different marker sets and segment models in use.

摆线轮工作齿廓的二次分段修形方法

针对常规修形方法存在计算复杂、齿廓曲线形状控制不够灵活等问题,提出了一种摆线轮工作齿廓的二次分段修形方法。通过摆线轮齿廓压力角变化规律的分析,将工作齿廓最小压力角的位置作为二次分段修形的基准点,建立二次分段修形模型。利用直线法简单函数,推导出二次分段修形方法下各齿段修形量的计算公式及齿廓方程。采用轮齿接触分析的方法,研究了二次分段修形齿廓的传动误差和回差,并进行对比实验验证摆线轮工作齿廓二次分段修形方法的正确性和可行性。结果表明:二次分段修形的摆线轮工作齿廓与理论齿廓曲线更加吻合,传动误差和回程误差分别减少约14%和19%。

IVIM-DWI基于肝段水平对肝纤维化患者测量的可重复性及诊断效能

目的评估肝纤维化患者应用体素内不相干运动扩散加权成像(IVIM-DWI)测量的可重复性,并评估基于肝右叶肝段的IVIM-DWI相关参数对早期及中晚期肝纤维化的诊断效能。方法2020年6月~2022年9月期间招募早期肝纤维化及晚期肝纤维化患者,使用IVIM-DWI序列进行肝脏扫描。两名检查者基于肝右叶各段水平,应用单指数模型、双指数模型和拉伸指数模型测量相关参数数据。对获得的测量数据应用变异系数(CV)进行可重复性检验,剔除可重复性较低(CV>20%)的肝段参数后,用Logistic回归分析最终纳入的IVIM-DWI参数,计算诊断效能并描绘ROC曲线。结果59名早期肝纤维化和38名晚期肝纤维化患者被纳入研究。获得的数据中,D*(CV:29.25%~41.47%)和DDC(CV:22.44%~30.63%)的可重复性较差,而ADC(CV:5.29%~9.76%)、D(9.53%~13.22%)、f(CV:12.83%~16.95%)和α(CV:8.82%~15.52%)的可重复性较好。剔除可重复性较低的参数后,Logistic回归分析建立早期肝纤维化与晚期肝纤维化组的诊断模型并描绘ROC曲线,最终纳入模型的肝段水平参数为SⅦD及SⅧf,最终回归模型的曲线下面积(AUC)为0.857。结论基于肝段的高可重复性,IVIM-DWI对肝纤维化仍能获得较好的诊断效能,其中D、f参数以及肝右叶上段SⅦ、SⅧ在满足可重复性的前提下,可能是较稳定的肝纤维化分级诊断因素。

基于哨兵1号影像浮冰形状特征的北极内尔斯海峡海冰运动信息提取方法

内尔斯海峡是北极海冰输出的重要通道之一,从海峡流出的海冰对巴芬湾、拉布拉多海深层水的形成以及北极海冰物质平衡具有重要影响。由于海峡入口狭窄和海冰密集度较低,现有海冰运动反演算法较难从卫星影像上获取有效的海冰运动信息,从而限制了该区域海冰运动信息的提取。本研究提出了一种基于浮冰形状特征的海冰运动提取算法,该算法主要包括影像分割、浮冰提取和浮冰匹配3个步骤。使用该算法从2019年2月20-27日逐日哨兵1号影像中提取了内尔斯海峡的海冰运动信息,并结合人工制作的验证数据评估了该算法在浮冰提取和匹配过程的精度。结果显示该算法提取的浮冰形状参数平均误差为4.3%~8.48%、浮冰运动矢量的总体正确率为84%、浮冰质心与运动速度的均方根误差分别为0.171km和6.313cm·s^(–1)。对比SIFT,ORB和AKAZE三种基于特征追踪的海冰运动反演算法,该算法结果中海冰运动覆盖面积增加了27.7%,精度提高约39%。该算法不仅有效地提取了内尔斯海峡海冰运动信息,而且可为其他区域(如海冰边缘区)的海冰运动信息提取提供参考。

基于Leap Motion和Unity3D的虚拟沙画手势识别及交互

虚拟仿真技术的快速发展及体感设备的不断更新为沙画动画这一全新的艺术创作形式带来新的灵感。针对沙画现场作画工序复杂的问题,结合Leap Motion设备和Unity3D开发环境完成手势识别并驱动虚拟手实现虚拟沙画效果。首先,依据Leap Motion捕捉到的手势坐标及方向信息提取手部关键点;然后提出角域划分的方法并引入新的特征向量,将其与提取信息串联作为手势分类依据;最后,根据自行定义的沙画手势语义驱动虚拟手完成虚拟沙画创作。实验证明,利用Leap Motion完成近距离手势识别效果较其他方法结果更加精准,实时性较高,手势跟踪稳定,虚拟沙画绘画过程沉浸感强。