SOME ASPECTS OF OBJECTIVE FORECASTING OF TROPICAL CYCLONE MOTION

As shown in a statistical analysis of the relationship between environmental fields at varied timeand tropical cyclone motion, the forecasting ability of the initisl environmental field predictors for tropical cyclone motion decreases with the increase of valid time period of forecast;it is higher with these predictors at a fUture time than at an initial time. The work also indicates that for the tropical cyclone motion over a given period of valid forecast, better predictors appear at times mostly differing from thevalid periods; for periods at 48-120 h the environmental predictors at 48-72 h are m0re capable of forecasting. With statistical interpretation of NWP products, a predictive model for tropical cyclone motionis superior in performance over a statistical forecasting model that employes predictors of the initial field in the basic framework. The concluding remarks can be used as reference in the construction of an objective prediction model for tropical cyclone motion.

APPLICATION OF STATISTICAL INTERPRETATION TECHNIQUES WITH NWP PRODUCTS FOR OBJECTIVE FORECASTING OF TROPICAL CYCLONE MOTION

Statistical study is first performed of the efficiency of the technique of statistical interpretation using the products of NWP. The result shows that the application of the technique has improved the predictabilily of predictors in objective forecasting of tropical cyclone motion, increased the forecasting skill of models and extended the valid period of forecast. Then a discussion is made of some technical problems in the application in the motion forecasting, suggesting: a large sample of data and perfect forecast method be employed in constructing objective forecast models for tropical cyclone motion, predictors be included that are so finely built that they reflect all synoptic features and physical quantity fields and NWP products be used and corrected that are available at multiple times. It is one of the effective ways to improve the skill and stability of the forecast by composite use of outcomes from various forecasting models.

IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION

Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coarse scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, a method to discriminate man-made objects from natural scenes is provided. Experiments are used to demonstrate the good efficiency of developed technique.

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.

Introduction of Simulating the Motion of Rigid Ellipsoidal Objects in Ductile Shear Zone Based on the Jeffery's Theory

Rigid ellipsoidal objects(gravels and porphyroclasts)in ductile zone is an important factor to indicate the kinematics and dynamics.Jeffery’s theory(Jeffery G,1922),a quantitative research method,for the rotation ofthe rigid objects(no deformation)in the Newtonian fluid of the simple deformation field has been widely applied by geologists to the study of fabrics in rocks.The theory

Moving Behavior of an Object in Gas-Solid Fluidized Beds

The settling behavior of coarse particles in a gas-solid fluidized bed was experimentally studied by using magnetic tracer. It is well known that the calculation of terminal velocity is of interest in dense medium separation. However, this problem has not been completely solved up to now. In this work, the terminal velocity of an object mov-ing in a gas-solid fluidized bed was experimentally measured and theoretically calculated. The experimental results in-dicated that the plastic viscosity and yield stress of the bed increase as the size of fluidized particles increases, but it varies little when some coarser particles are mixed with the fluidized particles. The resistance to a rising object was an order magnitude greater than that to a settling object. The efficient buoyancy on a flaky object, which lies flatly on the gas distributor, was much less than that calculated by the Archimedes principle. The object does not always rise or set-tle with minimal projective area owing to radial motion of the fluidized particles. But in the lower part of the bed, the bar-shaped objects were likely with minimal projective area rising or settling.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.

Particle Filter Object Tracking Algorithm Based on Sparse Representation and Nonlinear Resampling

Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.

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.

基于多目标优化的跨介质航行体水面滑跳初始运动参数设计

基于耦合欧拉-拉格朗日方法,开展了初始运动参数对带滑板跨介质航行体近水面滑跳过程的影响规律研究。采用参数回归方法分别建立最大浸深、最大过载和俯仰角变化3个变量随航行体初始速度、入水角、滑板倾角变化的关系式,得到了跨介质航行体稳定滑跳的参数包络,在入水角2°~5°、滑板倾角10°~25°、速度60~80 m/s范围之间,且在范围内各参数之间会相互制约。在此基础上,基于多目标优化算法获得了最适宜航行体稳定滑跳的初始参数为入水速度60.1 m/s、滑板倾角18.8°、入水角2°,在此参数范围内,跨介质航行体的水面滑跳过程可以在不超过航行体过载要求的条件下获得相对最小的入水浸深和俯仰角变化。

Fast Moving Object Extraction in H.264/AVC Compressed Domain

This paper presents a novel approach for moving object extraction in the H.264/AVC compressed domain, which based on Ant Colony clustering Algorithm (ACA) and threshold method in macro block layer. Firstly, the Motion Vector (MV) field and the macro block types are extracted from the H.264/AVC compressed video, and then merge MVs with the same characteristic. Secondly, an improved ACA is used to classify the MV field into different motion homogenous regions. At the same time, use macro block types to determine the location of objects. Finally, using the complementarities of macro block template and MVs clustering template to obtain final objects. Experimental results for several video sequences demonstrate that in the case of ensuring accuracy, the proposed approach can extract moving object faster.

Novel Approach for Motion Planning of Multiple Distributed Agent-based Robotic

In this paper, the Distributed Agent based Multirobots Assembly System(DAMAS) is introduced. Then, on the baseis of survey of previous research work on motion planning of multiple robots, a novel approach for the motion planning of multi cooperating robots is presented. This approach takes advantages of the Agent theory and the authors built computer network environment, together with the distributed object technology provided and supported by Windows NT and Visual C++.

联合判别性外观和运动线索的行人多目标跟踪

在多目标跟踪任务中,外界噪声的干扰会导致传统方法的系统建模不可靠,从而降低目标位置预测的准确性;而密集人群引起的拥挤和遮挡问题则会严重影响目标外观的可靠性,导致错误的身份关联.为了解决这些问题,本文提出一种多目标跟踪算法Ecsort.该算法在传统运动预测的基础上,引入噪声补偿模块,降低噪声干扰引起的误差,提高位置预测的准确性.其次,引入特征相似度匹配模块,通过学习目标的判别性外观特征,并结合运动线索和判别性外观特征的优势,从而实现精确的身份关联.通过在多目标跟踪基准数据集上进行的大量实验结果表明,与基线模型相比,该方法在MOT17测试集上的IDF1 (ID F1 score)、HOTA (higher order tracking accuracy)、AssA(association accuracy)、DetA (detection accuracy)分别提高了1.1%、0.5%、0.6%、0.3%,在MOT20测试集上的IDF1、HOTA、AssA、DetA分别提高了2.3%、1.9%、3.4%、0.2%.

面向三维多目标追踪的运动补偿优化方法

三维多目标追踪是自动驾驶系统中的关键模块之一,其结果的优劣主要取决于追踪模块中数据关联过程的准确度.现有的追踪方法大多从外观特征或运动特征出发计算两帧之间物体的相似度,而基于运动特征的方法通常根据当前帧和历史帧三维包围框之间的交并比(Intersection over Union,IoU)进行关联,然而这种方式在观测点物体自身运动时存在严重缺陷.在观测点物体自身进行运动时,观测到的两帧数据将处于不同的局部坐标系,导致无法使用运动模型准确预测已追踪物体在下一帧中的位置.本文针对上述问题,通过引入观测点自身的惯性测量单元(Inertial Measurement Unit,IMU)或全球定位系统(Global Positioning System,GPS)数据,在一帧数据到达之后计算当前帧局部坐标系与上一帧局部坐标系之间的旋转和平移关系,并对已追踪的物体状态按得到的坐标变换关系进行运动补偿,使其抵消因观测点自身运动造成的偏移量.这种运动补偿增强了追踪模块的数据关联环节,提高追踪时三维包围框的关联成功率,降低误关联数量,改善三维多目标追踪的精度.在相关追踪框架及KITTI数据集上的原型验证表明,所提的运动补偿优化方法实现了1%左右的精度提升.

基于运动合成的旋转变速运动建模及其驱动机构演变

为满足织机开口运动停—升—停的工艺特性及其动力学性能,提出一种基于运动合成的旋转变速运动规律构造方法。从组合机构角度提出旋转变速运动驱动机构及其演变,建立不同驱动机构的相对运动传递函数模型。采用目标优化法对旋转变速机构尺寸参数进行设计,在相同的中心距半径和转子臂初始位置的情况下建立优化函数并进行求解。仿真结果表明:偏移系数是影响旋转变速运动的关键参数,且变速运动静止时间、相对位移和速度峰值随偏移系数增加而减小;传递函数是影响旋转变速机构运动特性的重要参数,当偏移系数为0.4147时,随传动比的减小,转子臂相对位移减小,同时影响旋转变速机构凸轮特性,传动比大于0.9后不能满足旋转变速机构约束条件;用导杆滑块机构和铰接四杆机构对齿轮机构进行代换,可近似实现变速规律相对运动的传递,其传动误差分别为0.010、0.003 rad;通过减小中心距对铰接四杆机构重新优化可以提高旋转变速机构传动性能,满足高速运动需求。

全景视频中多运动对象检测与跟踪方法

全景视频多运动对象检测与跟踪技术的通用解决方案是通过投影变换将360°视频转为二维视频后进行检测和跟踪。然而固定球面投影变换会导致全景视频中存在空间上不连续的投影边界,当运动对象处于投影边界时,常规球面投影方法可能将其切分为多个部分,从而导致该运动对象的漏检或误检。针对该问题,提出了融合等距柱状形投影(equi-rectangular projection,ERP)和立方体投影(cube map projection,CMP)的全景视频多运动对象检测与跟踪方法,利用合适的CMP拼接帧减少ERP两极区域失真并弥补其边界不连续性,解决了处于投影边界和两极扭曲的对象检测问题。进一步,通过感知哈希、球面质心和运动方向等多重特征融合来解决全景视频多运动对象跟踪问题。实验结果表明,提出的方案可以有效减少投影边界不连续导致的误检,且能有效应对遮挡、穿梭帧边界和两极扭曲的对象跟踪问题。

基于相机运动估计的改进ECO多目标跟踪器设计

由于运动模型采用线性假设,使得多目标跟踪(multiple object tracking,MOT)在动态场景下容易受到相机运动和随机抖动的影响,导致跟踪错误。为解决上述问题,设计了一种相机运动感知滤波多目标跟踪器(camera motion aware filter multi-object tracker,CMAFT)。首先提出一种新模型,将相机运动估计和单目标跟踪(single object tracking,SOT)的区域搜索特性相结合,以补偿由相机运动引起的偏移并提高预测的准确度;其次针对该模型提出一个改进的级联匹配方法,通过融合SOT预测以处理不同目标间的相互遮挡和身份切换问题;最后在MOT17数据集上进行实验以验证提出方法的有效性。

图像去运动物体模糊算法

该文提出一种去运动物体模糊算法,将模糊图像分成运动模糊层和非运动模糊层,降低非运动物体区域对去模糊的影响,对运动模糊层使用局部线性运动进行建模并估计各图像块的模糊核,最后利用非盲解卷积方法求解清晰图像。实验结果表明在合成图像上相对于其他传统算法,平均峰值信噪比提高12.11 dB,在自然图像上同样获得更好的效果。

基于无人机航拍视频的运动目标检测与跟踪

针对无人机视角下拍摄物体尺寸小、外观模糊,以及无人机平台自身存在运动等问题,基于无人机航拍视频的运动目标检测与跟踪仍具有很大的挑战性。提出了基于改进LK光流法的运动目标检测与跟踪算法,利用卡尔曼滤波算法进行目标跟踪;针对传统LK光流法在动态环境下效果不佳的问题,引入语义分割线程筛选动态点和静态点,通过筛选后的静态点求解单应矩阵完成无人机的运动补偿、计算动态点的光流值获得光流点以降低误匹配率,最后通过聚类算法和形态学运算得到完整的运动目标;将所提的重识别模块引入卡尔曼滤波算法实现目标跟踪。实验结果验证了所设计的运动目标检测与跟踪算法在无人机视角下能够实时、准确地提取出运动目标,并持续稳定地跟踪目标。