Trajectory Representation in Location-Based Services: Problems and Solution

Recently, much work has been done in feasibility studies on services offered to moving objects in an environment equipped with mobile telephony, network technology and GIS. However, despite of all work on GIS and databases, the situations in which the whereabouts of objects are constantly monitored and stored for future analysis are an important class of problems that present-day database/GIS has difficulty to handle. Considering the fact that data about whereabouts of moving objects are acquired in a discrete way, providing the data when no observation is available is a must. Therefore, obtaining a "faithful representation" of trajectories with a sufficient number of discrete (though possibly erroneous) data points is the objective of this research

indexing and querying moving objects databases

Ph.DDOCTOR OF PHILOSOPH

Knowledge Discovery in Databases of Moving Objects

Cílem této diplomové práce je obecně se seznámit s problematikou získávání znalostí a klasifikací. Práce dále navazuje na aplikaci SUNAR, která je pak v praktické části vylepšena o SVM klasifikaci průchodů osob mezi jednotlivými kamerami. V závěru se pak pojednává o způsobech vylepšení klasifikace i rozpoznávání v programu SUNAR.The aim of this master's thesis is to get familiar with problems of data mining and classification. This thesis also continues with application SUNAR, which is upgraded in practical part with SVM classification of persons passing between cameras. In the conclusion, we discuss ways to improve classification and person recognition in application SUNAR.

Linking Moving Object Databases with Ontologies

This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies with databases. A major benefit gained from this kind of linking is the augmentation of database knowledge and multi-granular perspectives that are provided by ontologies through the process of generalization. Methods are presented for linking based on a military transportation scenario where data on vehicle position is collected from a sensor network and stored in a geosensor database. An ontology linking tool, implemented as a stand alone application, is introduced. This application associates individual values from the geosensor database with classes from a military transportation device ontology and returns linked value-class pairs to the user as a set of equivalence relations (i.e., matches). This research also formalizes a set of motion relations between two moving objects on a road network. It is demonstrated that the positional data collected from a geosensor network and stored in a spatio-temporal database, can provide a foundation for computing relations between moving objects. Configurations of moving objects, based on their spatial position, are described by motion relations that include isBehind and inFrontOf. These relations supply a user context about binary vehicle positions relative to a reference object. For example, the driver of a military supply truck may be interested in knowing what types of vehicles are in front of the truck. The types of objects that participate in these motion relations correspond to particular classes within the military transportation device ontology. This research reveals that linking a geosensor database to the military transportation device ontology will facilitate more abstract or higher-level perspectives of these moving objects, supporting inferences about moving objects over multiple levels of granularity. The details supplied by the generalization of geosensor data via linking, helps to interpret semantics and respond to user questions by extending the preliminary knowledge about the moving objects within these relations

Fast detecting and tracking of moving objects in video scenes

18 pages. Quelques films de résultats sont disponible sur: http://www.ceremade.dauphine.fr/~pelletieIn this article we present a new method for detecting textured moving objects. Based on a known background estimation and a fixed camera, the algorithm is able to detect moving objects and locates them at video rate, moreover this method is used for object tracking purposes. Our method is multi-step: First, we use level lines to detect pixels of the background which are occluded by moving object. Then, we use an a contrario model as general framework to make an automatic clustering. Thus the moving objects are detected as regions and not only as pixels, eventually we correct this region to better fit the moving object. Experimental results show that the algorithm is very robust to noise and to the quality of the background estimation (e.g. ghosts). The algorithm has been successfully tested in video sequences coming from different databases, including indoor and outdoor sequences

A model-based conceptual clustering of moving objects in video surveillance

Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Data mining techniques have been applied in video databases to identify various patterns or groups. Clustering analysis is used to find the patterns and groups of moving objects in video surveillance systems. Most existing methods for the clustering focus on finding the optimum of overall partitioning. However, these approaches cannot provide meaningful descriptions of the clusters. Also, they are not very suitable for moving object databases since video data have spatial and temporal characteristics, and high-dimensional attributes. In this paper, we propose a model-based conceptual clustering (MCC) of moving objects in video surveillance based on a formal concept analysis. Our proposed MCC consists of three steps: 'model formation' , 'model-based concept analysis' , and 'concept graph generation' . The generated concept graph provides conceptual descriptions of moving objects. In order to assess the proposed approach, we conduct comprehensive experiments with artificial and real video surveillance data sets. The experimental results indicate that our MCC dominates two other methods, i.e., generality-based and error-based conceptual clustering algorithms, in terms of quality of concepts.http://dx.doi.org/10.1117/12.70822

Exploring dance movement data using sequence alignment methods

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers