Maximal entanglement from quantum random walks

The conditions under which entanglement becomes maximal are sought in the general one-dimensional quantum random walk with two walkers. Moreover, a one-dimensional shift operator for the two walkers is introduced and its performance in generating entanglement is analyzed as a function of several free parameters, some of them coming from the shift operator itself and some others from the coin operator. To simplify the investigation an averaged entanglement is defined.     

Representing short-term observations of moving objects by a simple visual language

In a variety of dynamical systems, formations of motion patterns occur. Observing colonies of animals, for instance, for the scientist it is not only of interest which kinds of formations these animals show, but also how they altogether move around. In order to analyse motion patterns for the purpose of making predictions, to describe the behaviour of systems, or to index databases of moving objects, methods are required for dealing with them. This becomes increasingly important since a number of technologies have been devised which allow objects precisely to get traced. However, the indeterminacy of spatial information in real world environments also requires techniques to approximate reasoning, for example, in order to compensate for small and unimportant distinctions which are due to noisy measurements. As a consequence, precise as well as coarse motion patterns have to be dealt with.A set of 16 atomic motion patterns is proposed. On the one hand, a relation algebra is defined on them. On the other hand, these 16 relations form the basis of a visual language using which motion patterns can easily be dealt with in a diagrammatic way. The relations are coarse but crisp and they allow imprecise knowledge about motion patterns to be dealt with, while their diagrammatic realisation also allow precise patterns to get handled. While almost all approaches consider motion patterns along arbitrary time intervals, this paper in particular focuses on short-term motion patterns as we permanently observe them in our everyday life.The bottom line of the current work, however, is yet more general. While it has been widely argued that it makes sense to use both sentential and diagrammatic representations in order to represent different things in the same system adequately (and hence differently), we argue that it makes even sense to represent the same things differently in order to grasp different aspects of one and the same object of interest from different viewpoints. We demonstrate this by providing both a sentential and a diagrammatic representation for the purpose of grasping different aspects of motion patterns. It shows that both representations complement each other.     

Processing moving queries over moving objects using motion-adaptive indexes

This paper describes a motion-adaptive indexing scheme for efficient evaluation of moving continual queries (MCQs) over moving objects. It uses the concept of motion-sensitive bounding boxes (MSBs) to model moving objects and moving queries. These bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. Instead of indexing frequently changing object positions, we index less frequently changing object and query MSBs, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. This helps decrease the number of updates to the indexes. More importantly, we use predictive query results to optimistically precalculate query results, decreasing the number of searches on the indexes. Motion-sensitive bounding boxes are used to incrementally update the predictive query results. Furthermore, we introduce the concepts of guaranteed safe radius and optimistic safe radius to extend our motion-adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion-adaptive indexing scheme is efficient for the evaluation of both moving continual range queries and moving continual kNN queries.     

Application of probability to enhance the performance of fuzzy based mobile robot navigation

This paper presents a new path planning algorithm based on Probability and Fuzzy Logic (PFL) as a duality technique to enhance the performance of Fuzzy Logic alone. Fuzzy Logic interacts with the grading of obstacles existed in the path and probability lies over the decision to move the mobile robot. The fuzzy grading correspondence with the probabilistic decision is the primary function of moving the mobile robot towards the goal and the secondary is path planning which lies over the probability distribution function. The distance–speed combination rule is developed for effective navigation. The single and multiple mobile robot systems have been tested successfully in a dense environment in presence of obstacles (static and dynamic) and moving goal. The obtained results are optimal when compared to other navigational approaches in sense of navigational path length and time in the static and dynamic environment.     

Using In-frame Shear Constraints for Monocular Motion Segmentation of Rigid Bodies

It is a well known result in the vision literature that the motion of independently moving objects viewed by an affine camera lie on affine subspaces of dimension four or less. As a result a large number of the recently proposed motion segmentation algorithms model the problem as one of clustering the trajectory data to its corresponding affine subspace. While these algorithms are elegant in formulation and achieve near perfect results on benchmark datasets, they fail to address certain very key real-world challenges, including perspective effects and motion degeneracies. Within a robotics and autonomous vehicle setting, the relative configuration of the robot and moving object will frequently be degenerate leading to a failure of subspace clustering algorithms. On the other hand, while gestalt-inspired motion similarity algorithms have been used for motion segmentation, in the moving camera case, they tend to over-segment or under-segment the scene based on their parameter values. In this paper we present a principled approach that incorporates the strengths of both approaches into a cohesive motion segmentation algorithm capable of dealing with the degenerate cases, where camera motion follows that of the moving object. We first generate a set of prospective motion models for the various moving and stationary objects in the video sequence by a RANSAC-like procedure. Then, we incorporate affine and long-term gestalt-inspired motion similarity constraints, into a multi-label Markov Random Field (MRF). Its inference leads to an over-segmentation, where each label belongs to a particular moving object or the background. This is followed by a model selection step where we merge clusters based on a novel motion coherence constraint, we call in-frame shear, that tracks the in-frame change in orientation and distance between the clusters, leading to the final segmentation. This oversegmentation is deliberate and necessary, allowing us to assess the relative motion between the motion models which we believe to be essential in dealing with degenerate motion scenarios.We present results on the Hopkins-155 benchmark motion segmentation dataset [27], as well as several on-road scenes where camera and object motion are near identical. We show that our algorithm is competitive with the state-of-the-art algorithms on [27] and exceeds them substantially on the more realistic on-road sequences.     

结合时域差分和空域分级的实时运动人像跟踪

利用摄像头对运动人像目标进行自动跟踪在越来越多的领域成为客观需要,国内外目前也已经有不少对运动目标自动跟踪的方法和系统。但是,这些方法和系统在运动物体距离较远时不能较好辨识目标,外界环境对于系统的性能也存在较大的影响。针对以上不足,将时域差分和空域分级结合,提出了一种实时的运动人像跟踪算法,使其不仅能够实现实时地跟踪运动人像目标,自动进行变焦,而且对外界环境有较好的适应性。     

Enhancing videoconferencing using spatially varying sensing

Human vision can be characterized as a variable resolution system-the region around the fovea (point of attention) is observed with great detail, whereas the periphery is viewed in lesser detail. In this work, we show that spatially varying sensing (resembling the human eye) can indeed be useful in videoconferencing. A system that can incorporate multiple and moving foveae is described. There are various possible ways of implementing multiple foveae and combining information from them. Some of these alternative strategies are discussed and results are compared. We also show the advantage of using spatially varying sensing as a preprocessor to JPEG. The methods described here can be useful in designing teleconferencing systems and image databases     

Electromagnetic modular Smart Surface architecture and control in a microfactory context

This paper presents an electromagnetic conveyance system called electromagnetic modular Smart Surface (emSS) permitting to move pallets on a planar surface in a microfactory context. The proposed surface concept allows flexibility in reconfiguring the system layout along with product routing. The possibilities of accurate positioning of the moving pallet and controlling multiple pallets on the surface make the emSS suitable for reconfigurable and flexible manufacturing systems. However, the emSS control needs to be robust and scalable to adapt the changes in manufacturing systems. A framework is therefore defined to monitor and control the emSS by simulation or in-line. It allows to define product routing on the emSS by satisfying numerous requirements such as reduction in energy consumption, collision avoidance, etc., and to minimize the human interventions by changing product routing when emSS component failures occur. A first experiment realized on an emSS prototype, allowed to compare two paths strategies regarding cost function linked to energy consumption and velocities. Two other studies exploit the emSS modeling in terms of pallet path generation and simulation of collision avoidance.     

A Multibody Factorization Method for Independently Moving Objects

The structure-from-motion problem has been extensively studied in the field of computer vision. Yet, the bulk of the existing work assumes that the scene contains only a single moving object. The more realistic case where an unknown number of objects move in the scene has received little attention, especially for its theoretical treatment. In this paper we present a new method for separating and recovering the motion and shape of multiple independently moving objects in a sequence of images. The method does not require prior knowledge of the number of objects, nor is dependent on any grouping of features into an object at the image level. For this purpose, we introduce a mathematical construct of object shapes, called the shape interaction matrix, which is invariant to both the object motions and the selection of coordinate systems. This invariant structure is computable solely from the observed trajectories of image features without grouping them into individual objects. Once the matrix is computed, it allows for segmenting features into objects by the process of transforming it into a canonical form, as well as recovering the shape and motion of each object. The theory works under a broad set of projection models (scaled orthography, paraperspective and affine) but they must be linear, so it excludes projective cameras.     

基于HEVC的车辆异常事件检测

当前传统交通事故检测和查阅主要通过人工监测的方法,这种方法效率低且实时性差,本文提出一种基于最新压缩域视频编码标准HEVC（High-efficiency video coding）的车辆异常事件检测方法。首先对HEVC码流中提取出的运动矢量信息进行运动矢量累积迭代和中值滤波的预处理,之后根据提取出的块划分信息和运动矢量信息计算运动对象的运动强度,然后根据运动强度值和八连通区域法提取出运动对象,最后根据空间距离法和运动强度判别法检测出视频序列中发生的车辆异常事件。实验证明,该方法可以准确地检测出视频序列中发生的车辆异常事件;对于有着快速移动的运动目标以及多个运动目标的视频效果更好。     

一种基于时空信息的运动目标提取新算法

自动、快速的视频目标分割是目标基视频编码中的一项关键技术 .为此提出了一种基于时空信息的运动目标提取算法 .该算法首先根据多帧运动信息和高阶统计检测方法得到二值运动掩模图象 ,然后提出一种改进后的分水岭算法对运动区域及其周围部分进行分割 ,最后将二者所得结果进行投影运算 ,得到最终运动目标 .实验结果说明了该算法的有效性 .     

融合时空上下文的复杂背景下多运动目标检测

针对目标进、出视场和被部分遮挡情况下检测率低的问题,提出一种联合时空上下文的多运动目标检测算法STC-MMTD。首先,利用时间上下文信息,基于前后向运动历史图提取候选目标区域;然后,利用空间上下文信息和目标表观信息,通过基于稀疏编码的CRF模型计算目标置信度图;最后,计算候选目标区域的目标置信度,检测出多运动目标。实验结果表明,所提算法具有良好的检测性能,在保证较高定位精度的同时,查全率、查准率和F测度均高于其他多目标检测算法的。     

Motion estimation and segmentation

In the general structure-from-motion (SFM) problem involving several moving objects in a scene, the essential first step is to segment moving objects independently. We attempt to deal with the problem of optical flow estimation and motion segmentation over a pair of images. We apply a mean field technique to determine optical flow and motion boundaries and present a deterministic algorithm. Since motion discontinuities represented by line process are embedded in the estimation of the optical flow, our algorithm provides accurate estimates of optical flow especially along motion boundaries and handles occlusion and multiple motions. We show that the proposed algorithm outperforms other well-known algorithms in terms of estimation accuracy and timing.     

Direct motion estimation from a range scan sequence

This article presents an innovative method to estimate the motion parameters of a mobile robot equipped with a radial laser range‐finder. Our approach is based on the spatial and temporal linearization of the range function, which leads to a velocity constraint equation for the scanned points. Assuming that the mobile robot moves in a rigid environment, a least‐squares formulation is employed to come up with the motion estimation as well as the motion vectors of the scanned points as they move from scan to scan in the sequence. This motion field can be very useful for a number of applications including detection and tracking of moving objects. Although this is a preliminary work, experimental results show that good results are achieved with both real and synthetic data. ©1999 John Wiley & Sons, Inc.     

Video stabilization using maximally stable extremal region features

Video stabilization is an important technique in present day digital cameras as most of the cameras are hand-held, mounted on moving platforms or subjected to atmospheric vibrations. In this paper we propose a novel video stabilization scheme based on estimating the camera motion using maximally stable extremal region features. These features traditionally used in wide baseline stereo problems were never explored for video stabilization purposes. Through our extensive experiments show we how some properties of these region features are suitable for the stabilization task. After estimating the global camera motion parameters using these region features, we smooth the motion parameters using a gaussian filter to retain the desired motion. Finally, motion compensation is carried out to obtain a stabilized video sequence. A number of examples on real and synthetic videos demonstrate the effectiveness of our proposed approach. We compare our results to existing techniques and show how our proposed approach compares favorably to them. Interframe Transformation Fidelity is used for objective evaluation of our proposed approach.     

Equivalence relations between learnability,output-dissipativity and strict positive realness

This paper was originally motivated by aiming at explicating why a simple iterative learning control scheme for complicated robot dynamics with strong non-linearities works well in acquiring any given desired motion over a finite or infinite time duration or any periodic motion. To gain a physical insight into the problem, a class of linear dynamical systems with specified input and output of the same dimension is treated by defining two properties: output-dissipativity and learnability. It is then shown that the former implies the latter and furthermore, for a class of linear systems with single input and single output, they are equivalent to each other and each of them is also equivalent to strict positive realness of input-output transfer function. For a class of MIMO (multiple inputs and multiple outputs) systems, it is possible to prove that each of these properties is equivalent to strict positive realness of the input-output transfer function matrix if it is strictly proper or otherwise its direct term from input to output satisfies an extra condtion.     

Intelligent visual surveillance — A survey

Detection, tracking, and understanding of moving objects of interest in dynamic scenes have been active research areas in computer vision over the past decades. Intelligent visual surveillance (IVS) refers to an automated visual monitoring process that involves analysis and interpretation of object behaviors, as well as object detection and tracking, to understand the visual events of the scene. Main tasks of IVS include scene interpretation and wide area surveillance control. Scene interpretation aims at detecting and tracking moving objects in an image sequence and understanding their behaviors. In wide area surveillance control task, multiple cameras or agents are controlled in a cooperative manner to monitor tagged objects in motion. This paper reviews recent advances and future research directions of these tasks. This article consists of two parts: The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding. The second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.     

Camera calibration with one-dimensional objects moving under gravity

Camera calibration using one-dimensional (1D) rigid objects is arresting the attentions of researchers since the easy-to-construct geometrical structure of the apparatuses. In this paper, we extend the motion patterns applicable for calibration with the motion of 1D objects. We show that a 1D object with three or more markers, rotating around one marker which is moving in a plane, provides constraint equations on camera intrinsic parameters. A stick moving under gravity without other forces acting on performs such a motion. Simulated tests show the feasibility and numerical robustness of this method.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

Vehicle detection and tracking in airborne videos by multi-motion layer analysis

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are receiving more and more attention due to their advantages of high mobility, fast deployment and large surveillance scope. However, such systems are difficult to develop because of factors like UAV motion, scene complexity, and especially the partial occlusion of targets. To address these problems, a new framework of multi-motion layer analysis is proposed to detect and track moving vehicles in airborne platform. After motion layers are constructed, they are maintained over time for tracking vehicles. Most importantly, since the vehicle motion layers can be maintained even when the vehicles are only partially observed, the proposed method is robust to partial occlusion. Our experimental results showed that (1) compared with other previous algorithms, our method can achieve better performance in terms of higher detection rate and lower false positive rate; (2) it is more efficient and more robust to partial occlusion; (3) it is able to meet the demand of real time application due to its computational simplicity.