Optic Flow in Harmony

Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark.     

Detecting anomalies in spatio-temporal flow data by constructing dynamic neighbourhoods

In massive spatio-temporal datasets, anomalies that deviate from the global or local distributions are not just useless noise but possibly imply significant changes, surprising patterns, and meaningful insights, and because of this, detection of spatio-temporal anomalies has become an important research hotspot in spatio-temporal data mining. For spatio-temporal flow data (e.g., traffic flow data), the existing anomaly detection methods cannot handle the embedded dynamic characteristic. Therefore, this paper proposes the approach of constructing dynamic neighbourhoods to detect the anomalies in spatio-temporal flow data (called spatio-temporal flow anomalies). In this approach, the dynamic spatio-temporal flow is first modelled based on the real-time attribute values of the flow data, e.g., the velocity of vehicles. The dynamic neighbourhoods are then constructed by considering attribute similarity in the spatio-temporal flow. On this basis, global and local anomalies are detected by employing the idea of the G^⁎ statistic and the problem of multiple hypothesis testing is further addressed to control the false discovery rate. The effectiveness and practicality of our proposed approach are demonstrated through comparative experiments on traffic flow data from the central road network of central London for both weekdays and weekends.     

A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion

Many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy. Typically, these energy functionals consist of two terms: a data term, which requires e.g. that a brightness constancy assumption holds, and a regularizer that encourages global or piecewise smoothness of the flow field. In this paper we present a systematic classification of rotation invariant convex regularizers by exploring their connection to diffusion filters for multichannel images. This taxonomy provides a unifying framework for data-driven and flow-driven, isotropic and anisotropic, as well as spatial and spatio-temporal regularizers. While some of these techniques are classic methods from the literature, others are derived here for the first time. We prove that all these methods are well-posed: they posses a unique solution that depends in a continuous way on the initial data. An interesting structural relation between isotropic and anisotropic flow-driven regularizers is identified, and a design criterion is proposed for constructing anisotropic flow-driven regularizers in a simple and direct way from isotropic ones. Its use is illustrated by several examples.     

Learned Models for Estimation of Rigid and Articulated Human Motion from Stationary or Moving Camera

We propose an approach for modeling, measurement and tracking of rigid and articulated motion as viewed from a stationary or moving camera. We first propose an approach for learning temporal-flow models from exemplar image sequences. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are then incorporated to model the movement of regions of rigid or articulated objects. These spatio-temporal flow models are subsequently used as the basis for simultaneous measurement and tracking of brightness motion in image sequences. Then we address the problem of estimating composite independent object and camera image motions. We employ the spatio-temporal flow models learned through observing typical movements of the object from a stationary camera to decompose image motion into independent object and camera motions. The performance of the algorithms is demonstrated on several long image sequences of rigid and articulated bodies in motion.     

一种基于数据融合和方法融合的时空综合预测算法

时空数据挖掘是数据挖掘中的重要研究内容,其中时空预测的应用领域最为广泛．针对目前时空预测方法中的不足,提出了一种基于数据融合和方法融合的时空综合预测算法．该方法首先采用统计学原理对目标对象本身的时序进行预测;然后通过神经网络解算相邻对象的空间影响,继而对混合数据序列使用时空自回归预测模型;最后使用线性回归将单个的时间预测、空间预测和时空预测有效地融合在一起,得到综合预测结果．应用该方法预测铁路客流,突破了传统铁路客流预测方法的局限,实验结果表明了算法的有效性．     

地理栅格影像的时空聚集精确算法

时空聚集运算兼顾了栅格数据的空间维度和时间维度,在空间现象的分析与预测方面发挥着重要作用。考虑到现有面向地理栅格影像的时空聚集算法缺乏准确性,本文提出了用于时空聚集运算的两种精确算法—"贴片"算法和"窗口分割"算法,并给出了两种算法的基本原理和具体实现,分析了它们各自的特点和适用环境,最后在空间数据库BeyonDB中检验了算法的效果与效率。实验结果表明,两种新算法均解决了传统算法的误差问题,但在效率上稍有损失。     

Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets

When mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. These dynamic regions can be characterized by a location or set of locations that exhibit different behaviors from their neighbors and the time periods where these differences are most pronounced. Examples include locally intense areas of precipitation, anomalous sea surface temperature (SST) readings, and locally high levels of water pollution, to name a few. The focus of this paper is to find and analyze the pattern of moving dynamic spatio-temporal regions in large sensor datasets. The approach presented in this paper uses a measure of local spatial autocorrelation over time to determine how pronounced the difference in measurements taken at a spatial location is with those taken at neighboring locations. Dynamic regions are analyzed both globally, in the form of spatial locations and time periods that have the largest difference in local spatial autocorrelation, and locally, in the form of dynamic spatial locations for a particular time period or dynamic time periods for a particular spatial node. Then, moving dynamic regions are identified by determining the spatio-temporal connectivity, extent, and trajectory for groups of locally dynamic spatial locations whose position has shifted from one time period to the next. The efficacy of the approach is demonstrated on two real-world spatio-temporal datasets (a) NEXRAD precipitation and (b) SST. Promising results were found in discovering highly dynamic regions in these datasets depicting several real environmental phenomenon which are validated as actual events of interest.     

无线传感器网络中异常读数检测算法研究*

针对无线传感器网络的离群点检测算法由于没有充分考虑数据的时空关联性和网络的分布特性,导致检测精度低、通信量大和计算复杂度高等局限,提出了基于时空关联的分布计算与过滤的在线离群点检测算法。该算法在各传感器节点上利用传感器读数的时间关联性生成候选离群点,并利用空间关联性对候选离群点进行过滤得到局部离群点,最终将所有传感器节点上的局部离群点集中到sink节点上获得全局离群点。利用时空关联性提高了检测精度,利用分布计算与过滤减少了通信量和计算量,理论分析和实验结果均表明该算法优于现有算法。     

移动空间数据类型和操作的初步研究

1 引言移动空间对象是随时间变化的空间数据,由时空数据库(Spatio-Temporal Databases)进行管理和处理。一个空间点可能随时间而改变其位置,为了完整地反映该点的信息,数据库中应该存储该点的全部历史信息,这个空间对象就是一个移动点。同理,一个区域也可能随时间移动、扩大或缩小。时空现象在现实生活中非常普遍,如飞机航行时随着时间变化而改变它的空间位置,这飞机就是一个移动点;当森林中某处发生火灾时,火灾区就是一个空间对象:区域。该区域可能