Trajectories Of Mobile Objects Research Articles

Интеллектуальная система управления траекторным движением морского подвижного объекта и классификации объектов

Актуальность вопроса разработки интеллектуальных систем управления и классификации динамических объектов продиктована тем, что в области применения систем искусственного интеллекта появился большой класс сложных объектов требующих управления в реальном времени, функционирующих в проблемной (неопределенной) среде. Например, морские подвижные объекты (в том числе беспилотные), беспилотные летательные аппараты палубного базирования и т.п. Построение систем управления указанными объектами с использованием классической теории управления не позволяет получить требуемое качество управления в реальном времени. В связи с этим в работе приведены основные принципы построения интеллектуальных систем управления сложными динамическими процессами и классификации наблюдаемых им объектов с использованием нечёткого способа представления знаний. Рассмотрены вопросы построения и проблемы реализация логико-лингвистических (нечетких) моделей принятия управленческих решений в системах искусственного интеллекта, функционирующих в реальном масштабе времени. Проведён анализ современных способов обработки нечёткой информации и средств реализации нечётких алгоритмов управления. Отмечено, что несоответствие структур современных вычислительных устройств параллельному характеру обработки нечёткой информации в процессе реализации нечётких алгоритмов управления не позволяет в полной мере использовать возможности построения и реализации логико-лингвистических моделей управления сложными процессами и классификации динамических объектов в реальном масштабе времени. Предложены обобщённые структурные элементы однородных процессоров, на базе которых созданы трёхмерные ассоциативные модули специализированного сопроцессора поразрядно-параллельного принципа действия, служащего для обработки знаний в процессе принятия решений, а также структуру интеллектуальной системы управления траекторным движением морского подвижного объекта и классификации объектов на основе нечеткой базы знаний. Annotation. The relevance of the issue of developing intelligent control systems and classification of dynamic objects is dictated by the fact that in the field of application of artificial intelligence systems a large class of complex objects that require real-time control and operate in a problematic (uncertain) environment has appeared. For example, marine mobile objects (including unmanned ones), deck-based unmanned aerial vehicles, etc. The construction of control systems for these objects using classical control theory does not allow obtaining the required quality of control in real time. In this regard, the paper presents the basic principles of constructing intelligent control systems for complex dynamic processes and classifying objects observed by them using a fuzzy method of representing knowledge. The issues of construction and problems of implementation of logical-linguistic (fuzzy) models of management decision-making in artificial intelligence systems operating in real time are considered. An analysis of modern methods of processing fuzzy information and means of implementing fuzzy control algorithms has been carried out. It is noted that the discrepancy between the structures of modern computing devices and the parallel nature of processing fuzzy information in the process of implementing fuzzy control algorithms does not allow full use of the possibilities of constructing and implementing logical-linguistic models for controlling complex processes and classifying dynamic objects in real time. Generalized structural elements of homogeneous processors are proposed, on the basis of which three-dimensional associative modules of a specialized coprocessor of a bit-parallel operating principle are created, which serves for processing knowledge in the decision-making process, as well as the structure of an intelligent system for controlling the trajectory movement of a marine mobile object and classifying objects based on a fuzzy knowledge base.

Object tracking algorithm for a passive positioning system

We propose an algorithm for small-sized mobile object detection and trajectory parameter estimation for a passive positioning system that consists of several optical, thermal, and radio sensors. The algorithm is based on a combination of spatial and temporal processing of observation data. For spatial processing, a set of equations is solved that defines the sufficient condition for coupling the direction vectors to probable objects in the image stereo pair. Object coordinates and velocities for a single observation period are estimated. For temporal processing, the direction vectors are distributed based on connection to probable objects in a sequence of the capture intervals. The results of numerical modeling of the proposed algorithm show the advantage of combining the two approaches in comparison with the traditional object detection and tracking algorithms.

Sunshine-Based Trajectory Simplification

Sunshine-based trajectory analysis plays a significant impact on comprehensive scenarios, including driving safety and routing planning for mobile solar collection. To the best of our knowledge, however, a little work analyzes the impact of sunshine on mobile objects on different trajectories. This paper investigates the relationship between sunshine and the trajectories of mobile objects. We introduce a sunshine orientation model to integrate sunshine information with trajectories. A sunshine-based trajectory simplification model is proposed to remove redundant points in trajectories, which carry a little sunshine-related information. The sunshine-based trajectory simplification model is indeed versatile. We apply this model to help users efficiently query the sunshine information of their trips. Our design is evaluated with extensive simulations based on three-month general transit feed specification data from August to October in 2017 in five USA cities, including Washington, DC, USA, and Minneapolis. Furthermore, we develop a sunshine query system for mobile scenarios by embedding our sunshine-based trajectory simplification model and conduct a field study to show the validity and efficiency of this model. The evaluation results show that our design reduces 87.4% position points in trajectories while only sacrificing 9.6% of sunshine information in the field tests.

SunChoice: A Novel Framework for Sunshine-Based Trajectories Analysis

Trajectory analysis based on sunshine information shows that sunshine has a crucial effect on many conditions involving driving comfort and route planning. However, based on our best understanding, a little, if any, work takes into consideration the influence of sunshine on large numbers of trajectories of mobile objects such as vehicles. In this paper, we analyze, for the first time, the correlation between sunshine and large numbers of trajectories of mobile objects while presenting compression of the sunshine-based trajectories on a national scale. Within the research, a sunshine-oriented model is introduced to combine sunshine with real trajectories through the extraction of information centering around sunshine on arbitrary trajectories. Our trajectory analysis based on sunshine is versatile, and the model mentioned above is implemented to enhance the riding comfort of passengers. Our model is evaluated with an extensive simulation using general transit feed specification data over three months from August to October 2017 in five U.S. cities. We also conduct a field study in Minneapolis to show the validity and efficiency of our model. The evaluation results show that our design: 1) decreases trajectory computation by 88.7% with the trajectory compression and 2) achieves more than 90% accuracy for the trip sunshine information prediction in both the simulation and real-world tests.

FrameSTEP: A framework for annotating semantic trajectories based on episodes

We are witnessing an increasing usage of location data by a variety of applications. Consequently, information systems are required to deal with large datasets containing raw data to build high level abstractions. Semantic Web technologies offer powerful representation tools for pervasive applications. The convergence of location-based services and Semantic Web standards allows an easier interlinking and annotation of trajectories. However, due to the wide range of requirements on modeling mobile object trajectories, it is important to define a high-level data model for representing trajectory episodes and contextual elements with multiple levels of granularity and different options to represent spatial and temporal extents, as well as to express quantitative and qualitative semantic descriptions. In this article, we focus on modeling mobile object trajectories in the context of Semantic Web. First, we introduce a new version of the Semantic Trajectory Episodes (STEP) ontology to represent generic spatiotemporal episodes. Then, we present FrameSTEP as a new framework for annotating semantic trajectories based on episodes. As a result, we combine our ontology, which can represent spatiotemporal phenomena at different levels of granularity, with annotation algorithms, which allow to create instances of our model. The proposed spatial annotation algorithm explores the Linked Open Data cloud and OpenStreetMap tags to find relevant types of spatial features in order to describe the environment where the trajectory took place. Our framework can guide the development of future expert systems in trajectory analysis. It enables reasoning about knowledge gathered from large trajectory data and linked datasets in order to create several intelligent services.

A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks

Most of mobile object trajectory clustering analysis to date has been focused on clustering the location points or sub-trajectories extracted from trajectory data. This paper presents TraceMob , a systematic approach to clustering whole trajectories of mobile objects traveling in road networks. TraceMob as a whole trajectory clustering framework has three unique features. First, we design a quality measure for the distance between two whole trajectories. By quality, we mean that the distance measure can capture the complex characteristics of trajectories as a whole including their varying lengths and their constrained movement in the road network space. Second, we develop an algorithm that transforms whole trajectories in a road network space into multidimensional data points in a euclidean space while preserving their relative distances in the transformed metric space. This transformation enables us to effectively shift the clustering task for whole mobile object trajectories in the complex road network space to the traditional clustering task for multidimensional data in a euclidean space. Third, we develop a cluster validation method for evaluating the clustering quality in both the transformed metric space and the road network space. Extensive experimental evaluation with trajectories generated on real road network maps of different cities shows that TraceMob produces higher quality clustering results and outperforms existing approaches by an order of magnitude.

Estimation of Trajectory and Location for Mobile Sound Source

In this paper, we present an approach to estimate mobile sound source trajectory. An artificially created sound source signal is used in this work. The main aim of this paper is to estimate the mobile object trajectory via sound processing methods. The performance of generalized cross correlation techniques is compared with that of noise reduction filters for the success of trajectory estimation. The azimuth angle between the sound source and receiver is calculated during the whole movement. The parameter of Interaural Time Difference (ITD) is utilized for determining azimuth angle. The success of estimated delay is compared with different types of Generalized Cross Correlation (GCC) algorithms. In this study, an approach for sound localization and trajectory estimation on 2D space is proposed. Besides, different types of pre-filter method are tried for removing the noise and signal smoothing of recorded sound signals. Some basic parameters of sound localization process are also explained. Moreover, the calculation error of average azimuth angle is compared with different GCC and pre-filtered methods. To conclude, it is observed that estimation of location and trajectory information of a mobile object from a stereo sound recording is realized successfully.

Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density

Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of mobile object trajectories is targeted at road-network aware location-based applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters. We propose NEAT-a road-network aware approach for fast and effective clustering of trajectories of mobile objects traveling in road networks. Our approach carefully considers the traffic locality characterized by the physical constraints of the road network, the traffic flow among consecutive road segments, and the flow-based density to organize trajectories into spatial clusters in a comprehensive three-phase clustering framework. NEAT discovers spatial clusters as groups of sub-trajectories which describe both dense and highly continuous flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road networks. Experimental results demonstrate the flexibility of the NEAT system and show that NEAT is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.

Adaptive-Velocity Time-Geographic Density Estimation for Mapping the Potential and Probable Locations of Mobile Objects

Time-geographic density estimation (TGDE) provides a method for generating probability density surfaces for mobile objects. The technique operates by fitting distance-weighted geo-ellipses to each pair of points in a tracking dataset and combining them to create a final density surface. The sizes of the geo-ellipses, and thus the amount of smoothing, are determined by a velocity parameter which specifies how fast the object can travel. In prior formulations of TGDE this maximum-velocity parameter was treated as constant or fixed. This can be problematic if an object displays variable movement patterns, such as alternating between movements and stops. In this research we develop a more robust formulation of time-geographic density estimation, termed adaptive-velocity TGDE, which allows the maximum-velocity parameter to vary for each segment of the space-time path. First, the new mathematical formulation is demonstrated and compared with fixed-TGDE through an application to synthetic pedestrian tracking data. Second, both methods are evaluated in the context of accurately mapping the movement patterns of a homing pigeon tracked by GPS. The results of both applications demonstrate that adaptive-velocity TGDE has the potential to more accurately delineate the trajectories of mobile objects than the fixed-velocity version. Implications of these results in the context of developing a probabilistic time geography for general mobile objects are also discussed.

An Efficient Trajectory Clustering Framework Based Relative Distance

along with more and more trajectory dataset being collected into application servers, the research in trajectory clustering has become increasingly important topic. This paper proposes a new mobile object trajectory Clustering algorithm (Trajectory Clustering based Improved Minimum Hausdorff Distance under Translation, TraClustMHD). In this framework, improved Minimum Hausdorff Distance under Translation is presented to measure the similarity between sub-segments. In additional, R-Tree is employed to improve the efficiency. The experimental results showed that this algorithm better than based on Hausdorff distance and based on line Hausdorff distance has good trajectory clustering performance.

Microaggregation- and permutation-based anonymization of movement data

Movement data, that is, trajectories of mobile objects, are automatically collected in huge quantities by technologies such as GPS, GSM or RFID, among others. Publishing and exploiting such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows re-identification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span. Our distance measure can be naturally instantiated using other existing similarity measures for trajectories that are appropriate for anonymization purposes. Then, we propose two heuristics for trajectory anonymization which yield anonymized trajectories formed by fully accurate true original locations. The first heuristic is based on trajectory microaggregation using the above distance and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at location k-diversity. The strong point of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories. Experimental results on a synthetic data set and a real-life data set are presented; for similar privacy protection levels and most reasonable parameter choices, our two methods offer better utility than comparable previous proposals in the literature.

Mesures de similarité de trajectoires basées sur l’utilisation de patrons spatio-temporels

Mobile objects are now equipped with sensors allowing real time monitoring of their movements. Nowadays, the data produced by these sensors can be stored in spatio-temporal databases. Data mining on these stored positions allows to infer the behaviour of these mobile objects (spatio-temporal patterns) and to analyze positions and trajectories of mobile objects. Using these patterns, unusual situations can be detected. Two kinds of unusual situations are distinguished: unusual positions and unusual trajectories. This article defines similarity index based on spatial and temporal measures using fuzzy logic.

Context-aware and quality-aware algorithms for efficient mobile object management

The management of positions of mobile objects is an essential prerequisite for many context-aware systems such as advanced traffic management systems or personal assistance systems. In this paper, we present two approaches for the scalable tracking of mobile object trajectories and the efficient processing of continuous spatial range queries, respectively. We show in detail how both approaches utilize the basic concepts of accuracy relaxation and utilization of context information, such as movement predictions, to minimize the number of position updates, the size of trajectory data, and the number of energy-consuming position sensing operations.

Auto-organized visual perception using distributed camera network

Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. Cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. We propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. The images are decomposed sequentially into layers where redundant information are discarded. Without the need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. The information sent by the network is then represented by a reduced and compact amount of data using a codification process. This framework allows structures to be retrieved and also the topology of the network. It can also provide the localization and trajectories of mobile objects. Experiments will present practical results in the case of a network containing 20 cameras observing a common scene.

Synthesis of Compromise-Optimal Trajectories of Mobile Objects in Conflict Environment

Synthesis of Compromise-Optimal Trajectories of Mobile Objects in Conflict Environment