Trajectories Of Moving Objects Research Articles

Cortical representations of object motion trajectories in 3D space

Cortical representations of object motion trajectories in 3D space

Group Pattern Mining on Moving Objects’ Uncertain Trajectories

Uncertain is inherent in moving object trajectories due to measurement errors or time-discretized sampling. Unfortunately, most previous research on trajectory pattern mining did not consider the uncertainty of trajectory data. This paper focuses on the uncertain group pattern mining, which is to find the moving objects that travel together. A novel concept, uncertain group pattern, is proposed, and then a two-step approach is introduced to deal with it. In the first step, the uncertain objects’ similarities are computed according to their expected distances at each timestamp, and then the objects are clustered according to their spatial proximity. In the second step, a new algorithm to efficiently mining the uncertain group patterns is designed which captures the moving objects that move within the same clusters for certain timestamps that are possibly nonconsecutive. However the search space of group pattern is huge. In order to improve the mining efficiency, some pruning strategies are proposed to greatly reduce the search space. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on both real and synthetic trajectory datasets.

SMaRT: A novel framework for addressing range queries over nonlinear trajectories

A spatiotemporal database is a database that manages both space and time information. Common examples include tracking of moving objects, intelligent transportation systems, cellular communications and meteorology monitoring. A spatiotemporal query determines the objects included in a region at a specified period of time between two date-time instants referred as time window. In the context of this work, we present SMaRT: A novel Spatiotemporal Mysql ReTrieval framework, based on MySQL and PostgreSQL database management system. Moreover, we propose a demo user interface that implements all of its capabilities, in order to help user determine the most efficient spatiotemporal query method on user-defined 2D trajectories. To our knowledge, we are the first to study and compare methods of addressing range queries on nonlinear moving object trajectories, that are represented both in dual and native dimensional space. In particular, it is the first time a theoretically efficient dual approach was implemented for nonlinear trajectories and incorporated into a well-known open-source RDBMS. An experimental evaluation is included that shows the performance and efficiency of our approach.

A System Architecture for Heterogeneous Moving-Object Trajectory Metamodel Using Generic Sensors: Tracking Airport Security Case Study

This paper proposes a system architecture and case study for a heterogeneous moving-object trajectory metamodel using generic sensors. In order to provide a unified metamodel and powerful framework for trajectory's services and queries, the proposed trajectory's data model has benefited from advantages of both conceptual and ontological space-time. However, it extends the basic data model of trajectory with new patterns as the space-time path to describe activities of the moving object and the composite region of interest. Additionally, the proposed system is distinguished by providing a framework for dealing with moving-object trajectory in an interoperable way, using heterogonous sensors that traditional data model incapable for this purpose. The proposed system architecture for the moving-object trajectory's data model is focused on service composability and data interoperability combining Open Geospatial Consortium (OGC) standards, Service-Oriented Architecture, and streaming technology, to allow applications built using the framework to be scalable and have better performance. The case study presented for tracking travelers at the airport means that passenger locations can be determined with a higher degree of accuracy and precision. Indeed, this system makes it easy to answer complex queries, such as seeing closely where passengers congregate, how much time they spend in stores and restaurants, and where there may be bottlenecks. It will also enable airport retailers to communicate with travelers directly.

OS1529-370 マグネット・コイル法を利用した移動物体の速度・姿勢・軌道の計測と定式化

OS1529-370 マグネット・コイル法を利用した移動物体の速度・姿勢・軌道の計測と定式化

이동객체궤적에 대한 효율적인 최근접이웃검색

Because of the rapid growth of mobile communication and wireless communication, Location-based services are handled in many applications. So, the management and analysis of spatio-temporal data are a hot issue in database research. Index structure and query processing of such contents are very important for these applications. This paper addressees algorithms that make index structure by using Douglas-Peucker Algorithm and process nearest neighbor search query efficiently on moving objects trajectories. We compare and analyze our algorithms by experiments. Our algorithms make small size of index structure and process the query more efficiently.

Dynamic Scene Understanding for Behavior Analysis Based on String Kernels

This paper aims at dynamically understanding the properties of a scene from the analysis of moving object trajectories. Two different applications are proposed: the former is devoted to identify abnormal behaviors, while the latter allows to extract the k, most of the similar trajectories to the one hand-drawn by an human operator. A set of normal trajectories' models is extracted using a novel unsupervised learning technique: the scene is adaptively partitioned into zones using the distribution of the training set and each trajectory is represented as a sequence of symbols by considering positional information (the zones crossed in the scene), speed, and shape. The main novelty is the use of a kernel-based approach for evaluating the similarity between the trajectories. Furthermore, we define a novel and efficient kernel-based clustering algorithm, aimed at obtaining groups of normal trajectories. Experimentations, conducted over three standard data sets, confirm the effectiveness of the proposed approach.

Design and Implementation of Large-Scale Video Tracking Software System

The design of large-scale video tracking software system is studied. With the continuous development of computer video processing technology, large-scale video tracking technology has become very important. This paper presents a design method for large-scale video tracking software system based on Radon detection algorithm. During the video tracking process, numerous video images need to be collected, and then preprocessed with filtering algorithm, through Radon detection method to predict and compensate moving objects trajectory obtained in video to make up for the tracking lag caused by mutated direction. Experimental results show that the proposed algorithm for large-scale video tracking software design can improve the tracking accuracy effectively, and achieve satisfactory results.

Multisensor Fusion-Based Concurrent Environment Mapping and Moving Object Detection for Intelligent Service Robotics

Intelligent service robot development is an important and critical issue for human community applications. With the diverse and complex service needs, the perception and navigation are essential subjects. This investigation focuses on the synergistic fusion of multiple sensors for an intelligent service robot that not only performs self-localization and mapping but also detects moving objects or people in the building it services. First of all, a new augmented approach of graph-based optimal estimation was derived for concurrent robot postures and moving object trajectory estimate. Moreover, all the moving object detection issues of a robot's indoor navigation are divided and conquered via multisensor fusion methodologies. From bottom to up, the estimation fusion methods are tactically utilized to get a more precise result than the one from only the laser ranger or stereo vision. Furthermore, for solving the consistent association problem of moving objects, a covariance area intersection belief assignment is applied for motion state evaluation and the complementary evidences such as kinematics and vision features are both synergized together to enhance the association efficiency with the evidence fusion method. The proof of concept with experiments has been successfully demonstrated and analyzed.

Detecting and indexing moving objects for Behavior Analysis by Video and Audio Interpretation

In this thesis a system for analyzing moving objects behavior for surveillance applications is proposed: videos are processed in order to extract and analyze moving objects trajectories for identifying abnormal trajectories, associated to abnormal behaviors. Whereas the information extracted from the videos are not sufficient or not sufficiently reliable, the proposed system is enriched by a module in charge of recognizing audio events of interest such as shoots, screams or broken glasses. Finally, all the extracted information are suitably stored in order to allow an efficient retrieval from the human operator. Five different standard datasets have been used for testing the different modules proposed in this thesis; the obtained results, both in terms of accuracy and computational efficiency, confirm the effectiveness and the real applicability of the proposed approach.

An analysis of alignment and integral based kernels for machine learning from vessel trajectories

In this paper we present an analysis of the application of the two most important types of similarity measures for moving object trajectories in machine learning from vessel movement data. These similarities are applied in the tasks of clustering, classification and outlier detection. The first similarity type are alignment measures, such as dynamic time warping and edit distance. The second type are based on the integral over time between two trajectories. Following earlier work we define these measures in the context of kernel methods, which provide state-of-the-art, robust algorithms for the tasks studied. Furthermore, we include the influence of applying piecewise linear segmentation as pre-processing to the vessel trajectories when computing alignment measures, since this has been shown to give a positive effect in computation time and performance.In our experiments the alignment based measures show the best performance. Regular versions of edit distance give the best performance in clustering and classification, whereas the softmax variant of dynamic time warping works best in outlier detection. Moreover, piecewise linear segmentation has a positive effect on alignments, due to the fact that salient points in a trajectory, especially important in clustering and outlier detection, are highlighted by the segmentation and have a large influence in the alignments. Based on our experiments, integral over time based similarity measures are not well-suited for learning from vessel trajectories.

이동객체궤적에 대한 효율적인 범위질의

최근 많은 응용프로그램에서 시공간 데이터와 멀티미디어 데이터 등이 사용되고 있다. 그래서 이러한 유형의 데이터들을 효율적으로 관리하고 분석할 수 있는 연구들이 많이 진행 중이다. 본 논문에서는 이동객체궤적에 대하여 단순화 기법을 사용하여 단순화한 후에 색인구조를 생성하고 이 색인구조를 이용하여 범위질의를 효율적으로 처리할 수 있는 알고리즘을 제안한다. 이동객체궤적의 단순화 기법으로는 Douglas-Peucker 알고리즘을 수정하여 이용한다. 제안된 방법과 기존의 최소 경계 사각형(MBR)을 이용한 색인 방법을 실험을 통하여 비교 및 분석한다. 실험 결과로 제안된 방법에서는 색인 데이터 량이 상대적으로 작아지고 색인 및 질의 처리방법이 간단하며 기존의 방법보다 시공간적으로 효율적임을 확인하였다. The management and analysis of spatio-temporal and multimedia data is a hot issue in database research because such data types are handled in manny applications. Querying databases of such a content is very important for these applications. This paper addresses algorithms that make index structure by using Douglas-Peucker Algorithm and process range query efficiently on moving objects trajectories. We compare and analyze our algorithms and MBR by experiments. Our algorithms make smaller size of index structure and process more efficiently.

영상의 전역 특징과 이동객체의 지역 특징을 융합한 동영상 검색 디스크립터 설계

A descriptor which is suitable for motion analysis by using the motion features of moving objects from the real time image sequence is proposed. To segment moving objects from the background, the background learning is performed. We extract motion trajectories of individual objects by using the sequence of the 1st order moment of moving objects. The center points of each object are managed by linked list. The descriptor includes the 1st order coordinates of moving object belong to neighbor of the pre-defined position in grid pattern, The start frame number which a moving object appeared in the scene and the end frame number which it disappeared. A video retrieval by the proposed descriptor combining global and local feature is more effective than conventional methods which adopt a single feature among global and local features.

Occlusion detection in video sequences

An occlusion is the region between two overlapping objects with disparate motion. Detecting these occluded objects is crucial for many of the video processing. The Occlusion detection is decomposed into two independent sub problems. The First is to detect foreground objects on a frame-wise basis, by labeling each pixel in an image frame as either foreground or background. The second is to couple object observations at different points in a sequence to yield the object's motion trajectory and Occlusion. The motion segmentation is based on an adaptive background subtraction method that models each pixel as mixture of Gaussians. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This is useful to track moving objects and detect occlusion in lighting changes, repetitive motions from cluster, and long term scene changes.

Prediction of moving object trajectory based on probabilistic suffix tree

In the prediction of moving object trajectory, concerning the low accuracy rate of low order Markov model and the expansion of state space in high order model, a dynamic adaptive Probabilistic Suffix Tree( PST) prediction method based on variable length Markov model was proposed. Firstly, moving object's trajectory path was serialized according to the time;then the probability characteristic of sequence context was trained and calculated from the historical trajectory data of moving objects, the probabilistic suffix tree model based path sequence was constructed, combined with the actual trajectory data,thus the future trajectory information could be predicted dynamically and adaptively. The experimental results show that the highest prediction accuracy was obtained in second order model, with the order of the model increasing, the prediction accuracy was maintained at about 82% and better prediction results were achieved. In the meantime, space complexity was decreased exponentially and storage space was reduced greatly. The proposed method made full use of historical data and current trajectory information to predict the future trajectory, and provided a more flexible and efficient location-based services.

Clustering Algorithms for Moving Object Shape and Time Constrains Basis

Clustering moving object trajectory data is an appealing research direction to fulfil the needs of many applications. In general, clustering is defined as the division of data into groups of similar objects. Each group, called as cluster, consists of objects that are similar among themselves and dissimilar to objects of other groups . Here to consider the moving object for clustering. The first section describes different object to flow in different directions, the clustering technique cluster object not only to the direction, time consideration and also cover with similar shape of object within the cluster window moving position. The objects flow in different way, different speed and different shape. Here the position or location of clustering and moving object directions are considered. The second section deals with the maximum wind details are Hurricane/Tropical Data for Northern Indian Ocean. Here to concentrate the flow record of maximum wind time duration basis, starting from the year, 2001 to 2010, the maximum cyclone flow updated different duration i.e.,on hourly basis. The databases keep all records of data, to apply the clustering of four ways. First timely basis with limitation, second time and wind range basis, third exact time basis and fourth time limit with exact wind range.

Probabilistic nearest neighbor queries on uncertain moving object trajectories

Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their applications in spatio-temporal data analysis. More recent work has considered the realistic case where the trajectories are uncertain; however, only simple uncertainty models have been proposed, which do not allow for accurate probabilistic search. In this paper, we fill this gap by addressing probabilistic nearest neighbor queries in databases with uncertain trajectories modeled by stochastic processes, specifically the Markov chain model. We study three nearest neighbor query semantics that take as input a query state or trajectory q and a time interval, and theoretically evaluate their runtime complexity. Furthermore we propose a sampling approach which uses Bayesian inference to guarantee that sampled trajectories conform to the observation data stored in the database. This sampling approach can be used in Monte-Carlo based approximation solutions. We include an extensive experimental study to support our theoretical results.

A Voting-Based Sequential Pattern Recognition Method

We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority voting of the recognition results of the local classifiers. The voting strategy has a strong benefit that even if an input pattern has a very large deviation from a prototype locally at several points, they do not severely influence the recognition result; they are treated just as several incorrect votes and thus will be neglected successfully through the majority voting. For regularizing the recognition result, we introduce partial-dependency to local classifiers. An important point is that this dependency is introduced to not only local classifiers at neighboring point pairs but also to those at distant point pairs. Although, the dependency makes the problem non-Markovian (i.e., higher-order Markovian), it can still be solved efficiently by using a graph cut algorithm with polynomial-order computations. The experimental results revealed that the proposed method can achieve better recognition accuracy while utilizing the above characteristics of the proposed method.

Extracting Social Web from Moving Object Trajectories

The advent of a lot of mobile social networking applications on the smart phones has greatly changed the way how people interact with each other. The core function requiring by these applications is the location-based service providing by the sensors inside the phones. Accumulating rich location sequences, the phone actually can tell more than merely the location information. In this paper, we try to discover the beneath social web from these location information. In other words, we propose a novel trajectory mining approach to dig the social web from collections of such location information. Such approach permits to perform relationship mining from trajectory-generated content. Given a temporal threshold and a spatial threshold, our approach carries out trajectory based mining to identify who are in closer relationship with the querying moving object. The comprehensive evaluation of the approach has demonstrated very promising results and is also analyzed in this paper.

Discriminating direction of motion trajectories from angular speed and background information

The effects of a background scene on the perception of the trajectory of an approaching object and its relation to changes in angular speed and angular size were examined in five experiments. Observers judged the direction (upward or downward) of two sequentially presented motion trajectories simulating a sphere traveling toward the observer at a constant 3-D speed from a fixed distance. In Experiments 1-4, we examined the effects of changes in angular speed and the presence of a scene background, with changes in angular size based either on the trajectories being discriminated or on an intermediate trajectory. In Experiment 5, we examined the effects of changes in angular speed and scene background, with angular size either constant or consistent with an intermediate 3-D trajectory. Overall, we found that (1) observers were able to judge the direction of object motion trajectories from angular speed changes; (2) observers were more accurate with a 3-D scene background, as compared with a uniform background, suggesting that scene information is important for recovering object motion trajectories; and (3) observers were more accurate in judging motion trajectories based on angular speed when the angular size function was consistent with motion in depth than when the angular size was constant.