Modeling spatiotemporal topological relationships between moving object trajectories along road networks based on region connection calculus

Considering the attempts to model spatiotemporal topological relationships between moving object trajectories, the conceptual and computational framework for moving objects along a road network has not received much attention. This paper aims to draw an improved model based on Region Connection Calculus (RCC) theory to represent the spatiotemporal topological relationships between moving object trajectories along road networks. This paper first uses a dimension reduction method based on a linear-reference transformation to model the moving object trajectories segments, and then defines new time–connection and space–connection relations between two trajectory segments. On this basis, the paper proposes an extension to the RCC-based spatiotemporal binary relationship set so that the combined semantics of the spatiotemporal predicates can be described completely. A case study was carried out using Floating Car Data in Guangzhou city. The computational results show that in a real application, the occurrence frequencies of the RCC-based binary relationships are distributed nonuniformly and the semantics of some binary relationships with the highest occurrence are coarse. Therefore, the partition of the spatiotemporal connection relations and the finer aspects of the spatiotemporal relationship model may require further research work.     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

航空遥感数据的贝叶斯网络分类

介绍了利用贝叶斯网络对航空遥感数据进行分类的算法和过程，认为贝叶斯网络具有以下优点： 充分利用和综合了先验知识 与样本信息； 采用有向无环图（DAG）的方式描述了多特征数据间的相互关系； 给出了联合概率表，并通过联合概率表给出了每个 像元属于不同类别的概率。研究结果表明，贝叶斯网络可以为遥感数据分类提供一种新方法。     

基于网络拓扑结构的多摄像头移动对象辅助协同跟踪

针对当前公共安全监控系统在应对突发事件时,因监视器数量多、信息量大、事件变化快等因素而导致的不能及时有效地对移动对象进行跟踪的问题,提出了一种基于拓扑结构的多摄像头移动对象辅助协同跟踪方法。首先将城市路网中的摄像头监控区域抽象为虚拟节点定位至结点弧段模型中,构建摄像头网络拓扑结构;根据拓扑关系搜索出与发现移动对象的摄像头关联的摄像头集合;再分析摄像头间的空间邻近关系和时间差异,获取时序关系最优的摄像头组作为重点监控区域进行移动对象跟踪,并定期进行协同更新。最后建立了一个模拟监控系统验证了利用多摄像头协同方法实现对移动对象实时跟踪的有效性。     

Measuring Dynamic Interaction in Movement Data

The emergence of technologies capable of storing detailed records of object locations has presented scientists and researchers with a wealth of data on object movement. Yet analytical methods for investigating more advanced research questions from such detailed movement datasets remain limited in scope and sophistication. Recent advances in the study of movement data has focused on characterizing types of dynamic interactions, such as single‐file motion, while little progress has been made on quantifying the degree of such interactions. In this article, we introduce a new method for measuring dynamic interactions (termed DI) between pairs of moving objects. Simulated movement datasets are used to compare DI with an existing correlation statistic. Two applied examples, team sports and wildlife, are used to further demonstrate the value of the DI approach. The DI method is advantageous in that it measures interaction in both movement direction (termed azimuth) and displacement. Also, the DI approach can be applied at local, interval, episodal, and global levels of analysis. However the DI method is limited to situations where movements of two objects are recorded at simultaneous points in time. In conclusion, DI quantifies the level of dynamic interaction between two moving objects, allowing for more thorough investigation of processes affecting interactive moving objects.     

定向移动基于马尔科夫链的时空不确定性

概率时间地理是经典时间地理基于概率的一种扩展,它采用概率描述移动对象在可达位置的非等可能性。已有的概率时间地理是基于正态分布或布朗桥的,其方差与移动速度无关或随移动速度的增大而发散,因而难以兼顾应用针对性和稳定性。本文提出了一种基于马尔科夫链的概率时间地理方法。首先,构建中间关于两边的双向条件马尔科夫链,它在移动速度足够大时的极限可视为布朗桥,因而具有稳定性数字特征。然后,建立定向移动到马尔科夫链的映射关系,主要是根据定向移动的时空位置、移动速度等信息建立马尔科夫链的步长、状态空间和转移矩阵,这样马尔科夫链与移动速度有关。最后,利用双向马尔科夫链连续计算定向移动在任意时刻的概率分布云,其方差的针对性和稳定性在实例中进行了验证。     

A time‐geographic approach to quantifying wildlife–road interactions

Recent advances in time geography offer new perspectives for studying animal movements and interactions in an environmental context. In particular, the ability to estimate an animal's spatial location probabilistically at temporal sampling intervals between known fix locations allows researchers to quantify how individuals interact with one another and their environment on finer temporal and spatial scales than previously explored. This article extends methods from time geography, specifically probabilistic space–time prisms, to quantify and summarize animal–road interactions toward understanding related diurnal movement behaviors, including road avoidance. The approach is demonstrated using tracking data for fishers (Martes pennanti) in New York State, where the total probability of interaction with roadways is calculated for individuals over the duration tracked. Additionally, a summarization method visualizing daily interaction probabilities at 60 s intervals is developed to assist in the examination of temporal patterns associated with fishers’ movement behavior with respect to roadways. The results identify spatial and temporal patterns of fisher–roadway interaction by time of day. Overall, the methodologies discussed offer an intuitive means to assess moving object location probabilities in the context of environmental factors. Implications for movement ecology and related conservation planning efforts are also discussed.     

基于几何约束和目标检测的室内动态SLAM

针对即时定位与地图构建(SLAM)在室内动态环境下定位精度低和地图效果差的问题,提出一种基于几何约束和目标检测的室内动态SLAM方法.使用目标检测网络获取语义信息,提出运动物体漏检的方法;根据先验知识,提出准确识别动态区域的信息判定方法;结合几何约束和深度学习方法剔除动态点,利用静态点估计相机位姿;根据存储信息构建可闭环的静态地图.在TUM数据集上进行实验,定位精度比ORB-SLAM2提高97.5%,相较于其他动态SLAM可取得更好的性能.在室内真实环境进行实验,构建的静态地图更准确,有效提高了室内动态SLAM的定位精度和地图效果.     

Object tracking in surveillance video based on Siamese network considering geographical spatiotemporal constraints

Identifying and tracking objects in surveillance videos is an important means of mining information during surveillance. Currently, most object-tracking methods rely only on image features, which cannot accurately express the motion of the tracked object in real geographical scenes and are easily influenced by occlusion and surrounding objects having similar features. However, tracked objects, such as pedestrians and vehicles, usually move in geographical space with fixed patterns of motion, and the motion in a short time is constrained by geographical space and time, the motion trajectory is predictable, and the range of motion is limited. Therefore, based on the SiamFC object tracking framework, this study introduces geographical spatiotemporal constraints into the tracking framework and proposes the GeoSiamFC method. The objective of this is to: (1) construct the mapping relationship between geographical space and image space to solve the problem that the pixel movement within the image after perspective imaging cannot accurately express the motion of the tracked object in a real geographical scene; (2) add candidate search areas according to the predicted trajectory location to correct the tracking errors caused by the occlusion of the object; and (3) focus on the search for the range of motion of the mapped pixel within the image space according to the limited geographical range of motion of the tracked objects in a certain time to reduce the interference of similar objects within the search area. In this study, separate experiments were conducted on a common test dataset using multiple methods to deal with challenges such as occlusion and illumination changes. In addition, a robust test dataset with noise addition and luminance adjustment based on the common test dataset was used. The results show that GeoSiamFC outperforms other object-tracking methods in the common test dataset with a precision score of 0.995 and a success score of 0.756 compared with most other object-tracking algorithms under the base condition of using only shallow networks. Moreover, GeoSiamFC maintained the highest precision score (0.970) and high success score (0.734) in the more challenging robust test dataset as well. The tracking speed of 59 frames per second far exceeds the real-time requirement of 25 FPS. Geographical spatiotemporal constraints were considered to improve tracker performance while providing real-time feedback on the motion trajectory of the target in geographical space. Thus, the proposed method is suitable for real-time tracking of the motion trajectory of a target in real geographical scenes in various surveillance videos.     

GIS概念数据模型的研究

GIS概念数据模型直接反映了人们对于客观世界的理解,本文分析和总结了早期和近期几种GIS数据模型的特点,并对GIS表达的地理空间进行了重新理解,在此基础上提出了整体GIS数据模型设计的几个要点,并给出了一个简要的整体GIS数据模型。     

等时线模型支持下的路网可达性分析

等时线是道路网中参考于某特定点位的系列时间可达性的可视化表达,其生成需要分析道路网络特性及通行状况,其形态可以反映可达性规律。本文对等时线进行了定义、特性分析及生成方法研究;类比水滴沿木板的纹理扩散的过程,提出水流扩展算法生成等时线,在网络栅格化模型的基础上,考虑等时线扩展独特的约束条件,如道路状况、移动条件及时间分段等获得可达性扩展路径,通过扩展凸包和角平分线移位生成等时线。该算法基于场论思想,可以针对网络空间中任意感兴趣部分灵活负载约束条件,方便生成实时等时线;类比等高线表示地貌特征,对基于以上构建机制生成的等时线的排列、疏密、弯曲形式、弯曲方向及延伸方向等进行分析,发现空间可达性规律。     

面向全时段查询的移动对象时空数据模型研究

大量移动对象时空数据的有效管理是移动空间信息服务的一个新兴的研究内容。本文提出一个面向全时段查询的移动对象时空数据模型,并给出基本的模型定义;探讨移动对象过去、现在和将来的运动位置估算方法,这些方法包括延时法、滑动平均法、三次指数法等;研究了移动对象的状态更新策略,提出一个适应于更新时间间隔的变动阈值法的移动对象的状态更新策略,该策略采用时间滑动窗口形式建立移动对象状态更新间隔的一元指数回归方程,通过求取回归参数获得移动对象状态更新趋势,利用该参数动态调整状态更新闽值。最后,给出实验模拟结果,并对今后的研究工作进行了展望。     

邻接凸多边形方向关系计算及其推理

在对拓扑邻接凸多边形方向关系计算特殊性分析的基础上,提出了一个邻接凸多边形方向关系计算算法和二维空间方向关系推理方法。与Voronoi图所表达的邻近拓扑关系相结合,该算法可以用于计算离散二维空间目标之间的方向关系     

Measuring the structural similarity of network time prisms using temporal signatures with graph indices

The network‐time prism (NTP) is an extension of the space‐time prism that provides a realistic model of the potential pattern of moving objects in transportation networks. Measuring the similarity among NTPs can be useful for clustering, aggregating, and querying potential mobility patterns. Despite its practical importance, however, there has been little attention given to similarity measures for NTPs. In this research, we develop and evaluate a methodology for measuring the structural similarity between NTPs using the temporal signature approach. The approach extracts the one‐dimensional temporal signature of a selected property of NTPs and applies existing path similarity measures to the signatures. Graph‐theoretic indices play an essential role in summarizing the structural properties of NTPs at each moment. Two extensive simulation experiments demonstrate the feasibility of the approach and compare the performance of graph indices for measuring NTP similarity. An empirical application using bike‐share system data shows that the method is useful for detecting different usage patterns of two heterogenous user groups.     

顾及道路约束条件的动态目标检测与跟踪方法

智能交通是智慧城市的重要组成部分,面对复杂多变的道路背景,如何能够快速检测、跟踪道路监控影像中的动态目标,是智能交通建设的关键技术难点。本文根据道路监控视频特点,提出了采用道路约束条件与颜色特征集相结合的动态目标跟踪方法,以道路约束条件确定运动目标搜索区域,利用HSV颜色特征集进行特征匹配,然后基于ⅡR滤波背景法对背景影像进行更新及动态目标的检测,并根据道路约束条件与颜色特征相结合跟踪方法实现对动态目标的跟踪及动作预测。试验结果表明该方法可准确对运动目标进行检测与跟踪,且对慢速运动目标也具有较好的响应能力,实现了对道路动态目标的实时检测与跟踪。     

使用参照物的射电望远镜中心坐标测量方法

为满足新建射电望远镜在单站或多站联合进行的深空探测或射电天文观测对中心坐标精确测定的要求,提出一种利用已知精确中心坐标的望远镜作为参照物,测量地平式射电望远镜中心点坐标的方法和测量数据处理方法。这一方法对场地和设备的要求较低,能够得到毫米级或亚毫米级的位置精度。尤其适合对天线阵列的中心位置进行测量。对国家天文台密云观测站的40 m射电望远镜进行了中心坐标测量,位置均方根误差为2.312 mm,满足了后续的观测工作对其位置的需求。     

Trajectories of Moving Objects on a Network: Detection of Similarities,Visualization of Relations,and Classification of Trajectories

Development in techniques of spatial data acquisition enables us to easily record the trajectories of moving objects. Movement of human beings, animals, and birds can be captured by GPS loggers. The obtained data are analyzed by visualization, clustering, and classification to detect patterns frequently or rarely found in trajectories. To extract a wider variety of patterns in analysis, this article proposes a new method for analyzing trajectories on a network space. The method first extracts primary routes as subparts of trajectories. The topological relations among primary routes and trajectories are visualized as both a map and a graph‐based diagram. They permit us to understand the spatial and topological relations among the primary routes and trajectories at both global and local scales. The graph‐based diagram also permits us to classify trajectories. The representativeness of primary routes is evaluated by two numerical measures. The method is applied to the analysis of daily travel behavior of one of the authors. Technical soundness of the method is discussed as well as empirical findings.     

车载立方体全景影像匹配点的粗差检测方法

针对低匹配内点率情况利用随机抽样一致性算法RANSAC估计车载全景序列影像的极几何模型不稳定造成大量匹配点的粗差无法检测或误检测问题提出了一种基于多约束条件的粗差检测方法 以冗余粗差为约束条件采用SIFT和最近邻匹配方法获取独立随机匹配点并构建特征光流矢量 利用光流幅度和方向直方图统计结果融合极线尺度和天空点约束条件实现全景影像匹配点的粗差检测 通过不同数据的实验分析在短基线条件下可以有效地检测出大部分由纹理重复性尺度变化和运动物体产生的匹配粗差点 与传统方法比较本文方法可获得更高的匹配正确点数和正确率尤其在场景复杂造成的低内点率情况下算法表现较为稳定     

移动目标空间拓扑关系的度量描述

针对传统9交模型对移动目标空间拓扑关系表达及量化描述的不足,基于格网化思想,提出了度量参数量化描述的方法。首先对移动目标和参考目标进行格网化,划分为固定大小的格网阵列;然后利用9交模型确定空间目标间拓扑关系的形式化描述;最后引入内部分量、边界分量、外部分量3种度量参数对移动目标空间拓扑关系进行度量化描述。试验证明,该方法计算简单、直观,能更加有效地度量移动目标间的拓扑关系。     

A divide-and-conquer method for space–time series prediction

Space–time series can be partitioned into space–time smooth and space–time rough, which represent different scale characteristics. However, most existing methods for space–time series prediction directly address space–time series as a whole and do not consider the interaction between space–time smooth and space–time rough in the process of prediction. This will possibly affect the accuracy of space–time series prediction, because the interaction between these two components (i.e., space–time smooth and space–time rough) may cause one of them as dominant component, thus weakening the behavior of the other. Therefore, a divide-and-conquer method for space–time prediction is proposed in this paper. First, the observational fine-grained data are decomposed into two components: coarse-grained data and the residual terms of fine-grained data. These two components are then modeled, respectively. Finally, the predicted values of the fine-grained data are obtained by integrating the predicted values of the coarse-grained data with the residual terms. The experimental results of two groups of different space–time series demonstrated the effectiveness of the divide-and-conquer method.