一种移动对象位置信息的处理方法

针对现在普遍采用的将移动对象位置信息发送到服务器的方法所存在的问题,本文提出了基于触发式的位置信息发送方法。该方法减少了移动对象向服务器发送位置信息的次数,节约了服务器大量的资源。在服务器端,采用分段三次Hermite插值函数模拟移动对象历史轨迹,采用线性函数模拟移动对象当前的运动轨迹。本文还详细分析了各因素对当前运动轨迹误差的影响,最后通过实验验证了该方法的有效性。     

搜索路网空间中时空相似轨迹

为了分析移动对象行为特征,需要一种度量轨迹间相似性的方法,虽然在欧氏空间检索移动对象相似轨迹的研究较多,但在路网空间这种研究还不多见。在实际应用方面,大多数移动对象位于路网空间而不是欧氏空间。本文研究了路网空间相似轨迹的特性,并提出了一种在路网空间搜索相似轨迹的度量方法。实验结果表明该方法不仅是搜索相似轨迹的实用技术,也是一种较好的轨迹聚类方法     

移动对象轨迹时空相似性度量方法

在分析移动对象行为时,移动对象轨迹因包含大量的信息而具有重要的作用。在实际应用中移动对象常受限于空间网络而无法利用现有欧氏空间中轨迹及其距离处理技术。分析了道路网络空间轨迹相似性性质,提出一种移动对象轨迹建模的时空表示方法,能有效地将轨迹从道路网络空间转化到欧氏空间;同时提出了一种基于兴趣点POI（Points Of Interesting）距离的轨迹间相似性测量方法,有效地对轨迹进行化简并减少轨迹中节点的数目,从而降低算法时间复杂度。该方法不仅可以用于搜索相似轨迹,还可方便地应用到轨迹聚类的相关工作中。     

实时视频中的车辆运动轨迹的提取和聚类

利用运动物体轨迹的方向性、运动性和相互关系等典型特征对物体的运动轨迹进行聚类。首先利用改进的加权矢量Hausdorff距离作为度量运动物体轨迹相似度的方法,从而使之适用于空间距离有差别的运动物体轨迹的谱聚类问题;然后基于等周分割（ISO）算法,构造轨迹相似度矩阵,完成轨迹的粗聚类;最后利用轨迹的方向性特征和轨迹类间距对轨迹进行二次聚类,得到最终的轨迹聚类结果。     

基于运动目标轨迹优化的监控视频浓缩方法

视频浓缩是包含原视频有效信息的简短表示,以便于视频的存储、浏览和检索。然而,大部分视频浓缩方法得到的浓缩视频中会丢失少量目标,不能完整表达原始视频的全部内容。本文介绍了一种基于目标轨迹优化的视频浓缩方法。首先使用改进的目标轨迹提取算法提取原视频中目标的 轨迹,然后利用马尔可夫随机场模型和松弛线性规划算法得到每条轨迹的最优时间标签,将其与背景序列和目标轨迹结合生成浓缩视频。实验结果表明,与传统的视频浓缩方法相比,本文方法生成的浓缩视频具有较高的浓缩比,保证了信息的完整性又具有良好的视觉效果。     

Detection of risk factors using trajectory mining

This paper proposes a method for grouping trajectories as two-dimensional time-series data. Our method employed a two-stage approach. Firstly, it compared two trajectories based on their structural similarity, and determines the best correspondence of partial trajectories. Then, it calculated the value-based dissimilarity for the all pairs of matched segments, and outputs their total sum as the dissimilarity of two trajectories. We evaluated this method on two data sets. Experimental results on the Australia sign language dataset and chronic hepatitis dataset demonstrate that our method could capture the structural similarity between trajectories even in the presence of noise and local differences, and could provide better proximity for discriminating objects.     

受限网络移动对象不确定性轨迹的查询

针对移动对象轨迹的内在不确定性及其导致的查询精度较低的问题,将已有移动对象的空间轨迹不确定性模型应用于网络移动对象的轨迹表示中。在此基础上,将概率查询方法运用到受限网络移动对象的不确定轨迹查询中,提出移动对象轨迹的不确定性点查询和概率范围查询方法。实验结果表明,该方法具有较高的查询效率。     

交通监控中运动目标轨迹的距离计算和聚类

对交通监控中运动目标的轨迹距离计算和聚类方法进行了改进.在轨迹距离计算中,引入目标的空间坐标、运动速度、运动方向和尺寸4个参数,以提高聚类时对不同位置、不同速度、不同方向和不同尺寸运动目标的轨迹的区分能力;针对交通目标运动轨迹比较规律的特点,采用基于统计的方法对K均值的轨迹聚类算法进行初始化,从而可以自适应的确定聚类数目K值和聚类初始中心.在真实场景下,验证了算法的有效性和适用性.     

GPS coordinates estimation and camera calibration from solar shadows

In this paper, we discuss the issue of camera parameter estimation (intrinsic and extrinsic parameters), along with estimation of the geo-location of the camera by using only the shadow trajectories. By observing stationary objects over a period of time, it is shown that only six points on the trajectories formed by tracking the shadows of the objects are sufficient to estimate the horizon line of the ground plane. This line is used along with the extracted vertical vanishing point to calibrate the stationary camera. The method requires as few as two shadow casting objects in the scene and a set of six or more points on the shadow trajectories of these objects. Once camera intrinsic parameters are recovered, we present a novel application where one can accurately determine the geo-location of the camera up to a longitude ambiguity using only three points from these shadow trajectories without using any GPS or other special instruments. We consider possible cases where this ambiguity can also be removed if additional information is available. Our method does not require any knowledge of the date or the time when the images are taken, and recovers the date of acquisition directly from the images. We demonstrate the accuracy of our technique for both steps of calibration and geo-temporal localization using synthetic and real data.     

真实场景运动目标轨迹有效性判断与自动聚类算法研究*

提出了真实场景中的运动目标轨迹有效性判断与自动聚类方法。利用轨迹长度、坐标值方差及目标相邻两帧运动方向等信息,对轨迹进行了预处理,得到有效的轨迹,然后以其作为样本,计算轨迹之间的空间相似距离,采用K均值聚类法,按轨迹的几何形状完成了轨迹聚类。提出了利用目标运动的起始点及整个运动过程中目标的运动方向信息,采用K均值聚类方法,进一步按目标的运动方向完成了轨迹聚类。两种真实场景的目标轨迹聚类结果证明了该方法的有效性。其研究结果为学习轨迹模式、目标运动轨迹识别、分类、异常检测奠定了基础。     

面向路网的移动对象全时态高效索引模型

针对现有索引模型的冗余搜索问题,考虑路网拓扑结构及交叉口转向约束条件,提出一种面向路网的移动对象全时态高效索引模型。采用添加临近路段信息的方法索引历史轨迹和实时位置信息,设计新型窗口查询算法,实现移动对象查找,并运用指数平滑法进行轨迹的预测。实验结果表明,该模型具有较好的更新及查询性能。     

真实场景运动目标轨迹有效性判断与自动聚类算法研究

提出了真实场景中的运动目标轨迹有效性判断与自动聚类方法.利用轨迹长度、坐标值方差及目标相邻两帧运动方向等信息,对轨迹进行了预处理,得到有效的轨迹,然后以其作为样本,计算轨迹之间的空间相似距离,采用K均值聚类法,按轨迹的几何形状完成了轨迹聚类.提出了利用目标运动的起始点及整个运动过程中目标的运动方向信息,采用K均值聚类方法,进一步按目标的运动方向完成了轨迹聚类.两种真实场景的目标轨迹聚类结果证明了该方法的有效性.其研究结果为学习轨迹模式、目标运动轨迹识别、分类、异常检测奠定了基础.     

Reconstructing 3D trajectories of independently moving objects using generic constraints

The 3D reconstruction of scenes containing independently moving objects from uncalibrated monocular sequences still poses serious challenges. Even if the background and the moving objects are rigid, each reconstruction is only known up to a certain scale, which results in a one-parameter family of possible, relative trajectories per moving object with respect to the background. In order to determine a realistic solution from this family of possible trajectories, this paper proposes to exploit the increased linear coupling between camera and object translations that tends to appear at false scales. An independence criterion is formulated in the sense of true object and camera motions being minimally correlated. The increased coupling at false scales can also lead to the destruction of special properties such as planarity, periodicity, etc. of the true object motion. This provides us with a second, ‘non-accidentalness’ criterion for the selection of the correct motion among the one-parameter family.     

网络受限移动对象不确定性轨迹的索引

近年来,人们对于如何表示和处理移动对象的不确定性进行了研究,提出了一些较为有效的模型和算法.但是,在如何索引移动对象的不确定时空轨迹方面,相关的研究工作十分有限.为了解决上述问题,本文提出了一种网络受限移动对象不确定轨迹的索引结构(UTR-Tree),并给出了相关的索引更新及查询算法.在该索引结构的支持下,移动对象数据库不仅可以快速地处理对移动对象过去可能位置的查询,而且能够对其现在及将来的可能位置进行高效的查询处理.     

网络受限移动对象过去、现在及将来位置的索引

提出了一种适合于网络受限移动对象数据库的动态轨迹R树索引结构(network-constrained moving objects dynamic trajectory R-Tree,简称NDTR-Tree).NDTR-Tree不仅能够索引移动对象的整个历史轨迹,而且能够动态地索引和维护移动对象的当前及将来位置.为了比较相关索引结构及算法的性能,进行了详细的实验.实验结果表明,与现有的基于道路网络的移动对象索引方法如MON-Tree和FNR-Tree等相比,NDTR-Tree有效地提高了对网络受限移动对象动态全轨迹的查询处理性能.     

Trajectory adjustment for nonprehensile manipulation using latent space of trained sequence-to-sequence model*

When robots are used to manipulate objects in various ways, they often have to consider the dynamic constraint. Machine learning is a good candidate for such complex trajectory planning problems. However, it sometimes does not satisfy the task objectives due to a change in the objective or a lack of guarantee that the objective functions will be satisfied. To overcome this issue, we applied a method of trajectory deformation by using sequence-to-sequence (seq2seq) models. We propose a method of adjusting the generated trajectories, by utilizing the architecture of seq2seq models. The proposed method optimizes the latent variables of the seq2seq models instead of the trajectories to minimize the given objective functions. The verification results show that the use of latent variables can obtain the desired trajectories faster than direct optimization of the trajectories.     

On the Recovery of Motion of Dynamic Objects from Stereo Images

An approach to the recovery of trajectories of objects in a dynamic scene from stereo images is proposed. The approach is based on the use of a point representation of objects, visual odometry, and a set of algorithms that produce point models of objects and calculate their trajectories using matched 3D point clouds. Results of numerical experiments for synthetic scenes are discussed.     

Visible and infrared image registration using trajectories and composite foreground images

The registration of images from multiple types of sensors (particularly infrared sensors and visible color sensors) is a step toward achieving multi-sensor fusion. This paper proposes a registration method using a novel error function. Registration of infrared and visible color images is performed by using the trajectories of moving objects obtained using background subtraction and simple tracking. The trajectory points are matched using a RANSAC-based algorithm and a novel registration criterion, which is based on the overlap of foreground pixels in composite foreground images. This criterion allows performing registration when there are few trajectories and gives more stable results. Our method was tested and its performance quantified using nine scenarios. It outperforms a related method only based on trajectory points in cases where there are few moving objects.     

One Way Distance: For Shape Based Similarity Search of Moving Object Trajectories

An interesting issue in moving object databases is to find similar trajectories of moving objects. Previous work on this topic focuses on movement patterns (trajectories with time dimension) of moving objects, rather than spatial shapes (trajectories without time dimension) of their trajectories. In this paper we propose a simple and effective way to compare spatial shapes of moving object trajectories. We introduce a new distance function based on “one way distance” (OWD). Algorithms for evaluating OWD in both continuous (piece wise linear) and discrete (grid representation) cases are developed. An index structure for OWD in grid representation, which guarantees no false dismissals, is also given to improve the efficiency of similarity search. Empirical studies show that OWD out-performs existent methods not only in precision, but also in efficiency. And the results of OWD in continuous case can be approximated by discrete case efficiently.

Multiple motion scene reconstruction with uncalibrated cameras

In this paper, we describe a reconstruction method for multiple motion scenes, which are scenes containing multiple moving objects, from uncalibrated views. Assuming that the objects are moving with constant velocities, the method recovers the scene structure, the trajectories of the moving objects, the camera motion, and the camera intrinsic parameters (except skews) simultaneously. We focus on the case where the cameras have unknown and varying focal lengths while the other intrinsic parameters are known. The number of the moving objects is automatically detected without prior motion segmentation. The method is based on a unified geometrical representation of the static scene and the moving objects. It first performs a projective reconstruction using a bilinear factorization algorithm and, then, converts the projective solution to a Euclidean one by enforcing metric constraints. Experimental results on synthetic and real images are presented.