Relevant bibliographies by topics / Spatio-temporal indexing

Academic literature on the topic 'Spatio-temporal indexing'

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Published: 10 March 2023

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Journal articles on the topic "Spatio-temporal indexing"

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Li, Wu, Wu, and Zhao. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11 (November 12, 2019): 512. http://dx.doi.org/10.3390/ijgi8110512.

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Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.
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Feng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He, and Weijun Yang. "An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization." ISPRS International Journal of Geo-Information 7, no. 9 (September 8, 2018): 371. http://dx.doi.org/10.3390/ijgi7090371.

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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.
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Fatima, Nikhat, Ayesha Ameen, and Syed Raziuddin. "STQP: Spatio-Temporal Indexing and Query Processing." International Journal of Computer Applications 150, no. 10 (September 15, 2016): 5–9. http://dx.doi.org/10.5120/ijca2016911514.

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He, Zhenwen, Menno-Jan Kraak, Otto Huisman, Xiaogang Ma, and Jing Xiao. "Parallel indexing technique for spatio-temporal data." ISPRS Journal of Photogrammetry and Remote Sensing 78 (April 2013): 116–28. http://dx.doi.org/10.1016/j.isprsjprs.2013.01.014.

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Ni, Jinfeng, and Chinya V. Ravishankar. "Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations." IEEE Transactions on Knowledge and Data Engineering 19, no. 5 (May 2007): 663–78. http://dx.doi.org/10.1109/tkde.2007.1006.

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Idris, F. M., and S. Panchanathan. "Spatio-temporal indexing of vector quantized video sequences." IEEE Transactions on Circuits and Systems for Video Technology 7, no. 5 (1997): 728–40. http://dx.doi.org/10.1109/76.633489.

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Cho, Hyung-Ju, and Chin-Wan Chung. "Indexing range sum queries in spatio-temporal databases." Information and Software Technology 49, no. 4 (April 2007): 324–31. http://dx.doi.org/10.1016/j.infsof.2006.05.005.

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A, John, Sugumaran M, and Rajesh R S. "INDEXING AND QUERY PROCESSING TECHNIQUES IN SPATIO-TEMPORAL DATA." ICTACT Journal on Soft Computing 06, no. 03 (April 1, 2016): 1198–217. http://dx.doi.org/10.21917/ijsc.2016.0167.

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Vazirgiannis, Michael, Yannis Theodoridis, and Timos Sellis. "Spatio-temporal composition and indexing for large multimedia applications." Multimedia Systems 6, no. 4 (July 1, 1998): 284–98. http://dx.doi.org/10.1007/s005300050094.

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Cho, Hyung-Ju, Jun-Ki Min, and Chin-Wan Chung. "An adaptive indexing technique using spatio-temporal query workloads." Information and Software Technology 46, no. 4 (March 2004): 229–41. http://dx.doi.org/10.1016/j.infsof.2003.07.001.

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Dissertations / Theses on the topic "Spatio-temporal indexing"

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Tao, Yufei. "Indexing and query processing of spatio-temporal data /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
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Tamošiūnas, Saulius. "Mobilių objektų indeksavimas duomenų bazėse." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20140702_191451-16943.

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Pagrindinis šio darbo tikslas yra išnagrinėti judančių objektų indeksavimo duomenų bazėse problemas, siūlomus sprendimus bei palyginti keleto iš jų veiksmingumą. Įvairiais pjūviais buvo lyginami praeities duomenis indeksuojantys R ir iš jo išvesti STR bei TB medžiai. Eksperimentai atlikti naudojant sugeneruotus judančių objektų duomenis. Gauti rezultatai parodė, kad indeksų veiksmingas priklauso nuo tam tikrų sąlygų ir aplinkybių, kuriomis jie naudojami.
Over the past few years, there has been a continuous improvement in the wireless communications and the positioning technologies. As a result, tracking the changing positions of continuously moving objects is becoming increasingly feasible and necessary. Databases that deal with objects that change their location and/or shape over time are called spatio-temporal databases. Traditional database approaches for effective information retrieval cannot be used as the moving objects database is highly dynamic. A need for so called spatio-temporal indexing techniques comes to scene. Mainly, by the problem they are addressed to, indices are divided into two groups: a) indexing the past and b) indexing the current and predicted future positions. Also the have been proposed techniques covering both problems. This work is a survey for well known and used indices. Also there is a performance comparison between several past indexing methods. STR Tree, TB Tree and the predecessor of many indices, the R Tree are compared in various aspects using generated datasets of simulated objects movement.
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Leone, Marco. "Efficient indexing and retrieval from large moving object databases through dynamic spatio-temporal queries." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/987.

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2010 - 2011
Intelligent Transportatin Systems have gained a great importance in the last decades given the growing need for security in many public environments, with particular attention for traffic scenarios, which are daily interested by accidents, traffic queues, highway code violations, driving in the wrong lane or on the wrong side, and so on. In the context of camera-based traffic analysis systems, in this thesis I will present a novel indexing scheme for the design of a system for the extraction, the storage and retrieval of moving objects' trajectories from surveillance cameras... [edited by author]
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Tsuruda, Renata Miwa. "STB-index : um índice baseado em bitmap para data warehouse espaço-temporal." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/525.

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Made available in DSpace on 2016-06-02T19:06:04Z (GMT). No. of bitstreams: 1 5138.pdf: 2676227 bytes, checksum: 72ab4695bfe8833d7d34d1e803a6ec9a (MD5) Previous issue date: 2012-12-13
Financiadora de Estudos e Projetos
The growing concern with the support of the decision-making process has made companies to search technologies that support their decisions. The technology most widely used presently is the Data Warehouse (DW), which allows storing data so it is possible to produce useful and reliable information to assist in strategic decisions. Combining the concepts of Spatial Data Warehouse (SDW), that allows geometry storage and managing, and Temporal Data Warehouse (TDW), which allows storing data changes that occur in the real-world, a research topic known as Spatio-Temporal Data Warehouse (STDW) has emerged. STDW are suitable for the treatment of geometries that change over time. These technologies, combined with the steady growth volume of data, show the necessity of index structures to improve the performance of analytical query processing with spatial predicates and also with geometries that may vary over time. In this sense, this work focused on proposing an index for STDW called Spatio-Temporal Bitmap Index, or STB-index. The proposed index was designed to processing drill-down and roll-up queries considering the existence of predefined spatial hierarchies and with spatial attributes that can vary its position and shape over time. The validation of STB-index was performed by conducting experimental tests using a DWET created from synthetic data. Tests evaluated the elapsed time and the number of disk accesses to construct the index, the amount of storage space of the index and the elapsed time and the number of disk accesses for query processing. Results were compared with query processing using database management system resources and STBindex improved the query performance by 98.12% up to 99.22% in response time compared to materialized views.
A crescente preocupação com o suporte ao processo de tomada de decisão estratégica fez com que as empresas buscassem tecnologias que apoiassem as suas decisões. A tecnologia mais utilizada atualmente é a de Data Warehouse (DW), que permite armazenar dados de forma que seja possível produzir informação útil e confiável para auxiliar na tomada de decisão estratégica. Aliando-se os conceitos de Data Warehouse Espacial (DWE), que permite o armazenamento e o gerenciamento de geometrias, e de Data Warehouse Temporal (DWT), que possibilita representar as mudanças nos dados que ocorrem no mundo real, surgiu o tema de pesquisa conhecido por Data Warehouse Espaço-Temporal (DWET), que é próprio para o tratamento de geometrias que se alteram ao longo do tempo. Essas tecnologias, aliadas ao constante crescimento no volume de dados armazenados, evidenciam a necessidade de estruturas de indexação que melhorem o desempenho do processamento de consultas analíticas com predicados espaciais e com variação das geometrias no tempo. Nesse sentido, este trabalho se concentrou na proposta de um índice para DWET denominado Spatio- Temporal Bitmap Index, ou STB-index. O índice proposto foi projetado para o processamento de consultas do tipo drill-down e roll-up considerando a existência de hierarquias espaciais predefinidas, sendo que os atributos espaciais podem variar sua posição e sua forma ao longo do tempo. A validação do STB-index ocorreu por meio da realização de testes experimentais utilizando um DWET criado a partir de dados sintéticos. Os testes avaliaram o tempo e o número de acessos a disco para a construção do índice, a quantidade de espaço para armazenamento do índice e o tempo e número de acessos a disco para o processamento de consultas analíticas. Os resultados obtidos foram comparados com o processamento de consultas utilizando os recursos disponíveis dos sistemas gerenciadores de banco de dados, sendo que o STB-index apresentou um ganho de desempenho entre 98,12% e 99,22% no tempo de resposta das consultas se comparado ao uso de visões materializadas.
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Cortés, Rudyar. "Scalable location-temporal range query processing for structured peer-to-peer networks." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066106/document.

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La recherche et l'indexation de données en fonction d'une date ou d'une zone géographique permettent le partage et la découverte d'informations géolocalisées telles que l'on en trouve sur les réseaux sociaux comme Facebook, Flickr, ou Twitter. Cette réseau social connue sous le nom de Location Based Social Network (LBSN) s'applique à des millions d'utilisateurs qui partagent et envoient des requêtes ciblant des zones spatio-temporelles, permettant d'accéder à des données géolocalisées générées dans une zone géographique et dans un intervalle de temps donné. Un des principaux défis pour de telles applications est de fournir une architecture capable de traiter la multitude d'insertions et de requêtes spatio-temporelles générées par une grande quantité d'utilisateurs. A ces fins, les Tables de Hachage Distribué (DHT) et le paradigme Pair-à-Pair (P2P) sont autant de primitives qui forment la base pour les applications de grande envergure. Cependant, les DHTs sont mal adaptées aux requêtes ciblant des intervalles donnés; en effet, l'utilisation de fonctions de hachage sacrifie la localité des données au profit d'un meilleur équilibrage de la charge. Plusieurs solutions ajoutent le support de requêtes ciblant des ensembles aux DHTs. En revanche ces solutions ont tendance à générer un nombre de messages et une latence élevée pour des requêtes qui ciblent des intervalles. Cette thèse propose deux solutions à large échelle pour l'indexation des données géolocalisées
Indexing and retrieving data by location and time allows people to share and explore massive geotagged datasets observed on social networks such as Facebook, Flickr, and Twitter. This scenario known as a Location Based Social Network (LBSN) is composed of millions of users, sharing and performing location-temporal range queries in order to retrieve geotagged data generated inside a given geographic area and time interval. A key challenge is to provide a scalable architecture that allow to perform insertions and location-temporal range queries from a high number of users. In order to achieve this, Distributed Hash Tables (DHTs) and the Peer-to-Peer (P2P) computing paradigms provide a powerful building block for implementing large scale applications. However, DHTs are ill-suited for supporting range queries because the use of hash functions destroy data locality for the sake of load balance. Existing solutions that use a DHT as a building block allow to perform range queries. Nonetheless, they do not target location-temporal range queries and they exhibit poor performance in terms of query response time and message traffic. This thesis proposes two scalable solutions for indexing and retrieving geotagged data based on location and time
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Ton, That Dai Hai. "Gestion efficace et partage sécurisé des traces de mobilité." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLV003/document.

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Aujourd'hui, les progrès dans le développement d'appareils mobiles et des capteurs embarqués ont permis un essor sans précédent de services à l'utilisateur. Dans le même temps, la plupart des appareils mobiles génèrent, enregistrent et de communiquent une grande quantité de données personnelles de manière continue. La gestion sécurisée des données personnelles dans les appareils mobiles reste un défi aujourd’hui, que ce soit vis-à-vis des contraintes inhérentes à ces appareils, ou par rapport à l’accès et au partage sûrs et sécurisés de ces informations. Cette thèse adresse ces défis et se focalise sur les traces de localisation. En particulier, s’appuyant sur un serveur de données relationnel embarqué dans des appareils mobiles sécurisés, cette thèse offre une extension de ce serveur à la gestion des données spatio-temporelles (types et operateurs). Et surtout, elle propose une méthode d'indexation spatio-temporelle (TRIFL) efficace et adaptée au modèle de stockage en mémoire flash. Par ailleurs, afin de protéger les traces de localisation personnelles de l'utilisateur, une architecture distribuée et un protocole de collecte participative préservant les données de localisation ont été proposés dans PAMPAS. Cette architecture se base sur des dispositifs hautement sécurisés pour le calcul distribué des agrégats spatio-temporels sur les données privées collectées
Nowadays, the advances in the development of mobile devices, as well as embedded sensors have permitted an unprecedented number of services to the user. At the same time, most mobile devices generate, store and communicate a large amount of personal information continuously. While managing personal information on the mobile devices is still a big challenge, sharing and accessing these information in a safe and secure way is always an open and hot topic. Personal mobile devices may have various form factors such as mobile phones, smart devices, stick computers, secure tokens or etc. It could be used to record, sense, store data of user's context or environment surrounding him. The most common contextual information is user's location. Personal data generated and stored on these devices is valuable for many applications or services to user, but it is sensitive and needs to be protected in order to ensure the individual privacy. In particular, most mobile applications have access to accurate and real-time location information, raising serious privacy concerns for their users.In this dissertation, we dedicate the two parts to manage the location traces, i.e. the spatio-temporal data on mobile devices. In particular, we offer an extension of spatio-temporal data types and operators for embedded environments. These data types reconcile the features of spatio-temporal data with the embedded requirements by offering an optimal data presentation called Spatio-temporal object (STOB) dedicated for embedded devices. More importantly, in order to optimize the query processing, we also propose an efficient indexing technique for spatio-temporal data called TRIFL designed for flash storage. TRIFL stands for TRajectory Index for Flash memory. It exploits unique properties of trajectory insertion, and optimizes the data structure for the behavior of flash and the buffer cache. These ideas allow TRIFL to archive much better performance in both Flash and magnetic storage compared to its competitors.Additionally, we also investigate the protect user's sensitive information in the remaining part of this thesis by offering a privacy-aware protocol for participatory sensing applications called PAMPAS. PAMPAS relies on secure hardware solutions and proposes a user-centric privacy-aware protocol that fully protects personal data while taking advantage of distributed computing. For this to be done, we also propose a partitioning algorithm an aggregate algorithm in PAMPAS. This combination drastically reduces the overall costs making it possible to run the protocol in near real-time at a large scale of participants, without any personal information leakage
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Dvořák, Jan. "Doménové indexy v prostředí Oracle 11g." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237062.

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This thesis deals with the domain indexes in Oracle Database 11g. It describes the database architecture and discusses the available methods of indexing. There are explained concrete ways of the implementation and use of domain indexes, also discussed ways of indexing spatio-temporal data especially the TB-tree structure, which is then implemented as a domain index. Along with the domain index operators are also implemented by means of which the index is subsequently used and tested.
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Ahmed, Tanvir. "Analytics on Indoor Moving Objects with Applications in Airport Baggage Tracking." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/231657.

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A large part of people's lives are spent in indoor spaces such as office and university buildings, shopping malls, subway stations, airports, museums, community centers, etc. Such kind of spaces can be very large and paths inside the locations can be constrained and complex. Deployment of indoor tracking technologies like RFID, Bluetooth, and Wi-Fi can track people and object movements from one symbolic location to another within the indoor spaces. The resulting tracking data can be massive in volume. Analyzing these large volumes of tracking data can reveal interesting patterns that can provide opportunities for different types of location-based services, security, indoor navigation, identifying problems in the system, and finally service improvements. In addition to the huge volume, the structure of the unprocessed raw tracking data is complex in nature and not directly suitable for further efficient analysis. It is essential to develop efficient data management techniques and perform different kinds of analysis to make the data beneficial to the end user. The Ph.D. study is sponsored by the BagTrack Project (http://daisy.aau.dk/bagtrack). The main technological objective of this project is to build a global IT solution to significantly improve the worldwide aviation baggage handling quality. The Ph.D. study focuses on developing data management techniques for efficient and effective analysis of RFID-based symbolic indoor tracking data, especially for the baggage tracking scenario. First, the thesis describes a carefully designed a data warehouse solution with a relational schema sitting underneath a multidimensional data cube, that can handle the many complexities in the massive non-traditional RFID baggage tracking data. The thesis presents the ETL flow that loads the data warehouse with the appropriate tracking data from the data sources. Second, the thesis presents a methodology for mining risk factors in RFID baggage tracking data. The aim is to find the factors and interesting patterns that are responsible for baggage mishandling. Third, the thesis presents an online risk prediction technique for indoor moving objects. The target is to develop a risk prediction system that can predict the risk of an object in real-time during its operation so that the object can be saved from being mishandled. Fourth, the thesis presents two graph-based models for constrained and semi-constrained indoor movements, respectively. These models are used for mapping the tracking records into mapping records that represent the entry and exit times of an object at a symbolic location. The mapping records are then used for finding dense locations. Fifth, the thesis presents an efficient indexing technique, called the $DLT$-Index, for efficiently processing dense location queries as well as point and interval queries. The outcome of the thesis can contribute to the aviation industry for efficiently processing different analytical queries, finding problems in baggage management systems, and improving baggage handling quality. The developed data management techniques also contribute to the spatio-temporal data management and data mining field.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
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Martin, Pierre-Etienne. "Détection et classification fines d'actions à partir de vidéos par réseaux de neurones à convolutions spatio-temporelles : Application au tennis de table." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0313.

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La reconnaissance des actions à partir de vidéos est l'un des principaux problèmes de vision par ordinateur. Malgré des recherches intensives, la différenciation et la reconnaissance d'actions similaires restent un défi. Cette thèse porte sur la classification des gestes sportifs à partir de vidéos, avec comme cadre applicatif le tennis de table.Nous proposons une méthode d’apprentissage profond pour segmenter et classifier automatiquement les différents coup de Tennis de Table. Notre objectif est de concevoir un système intelligent permettant d'analyser les performances des élèves pongistes, et de donner la possibilité à l’entraîneur d'adapter ses séances d'entraînement pour améliorer leurs performances.Dans ce but, nous avons élaboré la base de données “TTStroke-21”, constituée de clips vidéo d'exercices de tennis de table, enregistrés par les étudiants de la faculté de sport de l'Université de Bordeaux – STAPS. Cette base de données a ensuite été annotée par des professionnels du domaine à l'aide d'une plateforme crowdsourcing. Les annotations consistent en une description des coups effectués (début, fin et type de coup). Au total, 20 différents coups de tennis de table sont considérés plus une classe de rejet.La reconnaissance des actions similaires présente des différences avec la reconnaissance d’actions classique. En effet, dans les bases de données classiques, le contexte de l’arrière plan fournit souvent des informations discriminantes que les méthodes peuvent utiliser pour classer l'action plutôt que de se concentrer sur l'action elle-même. Dans notre cas, la similarité entre classes est élevée, les caractéristiques visuelles discriminantes sont donc plus difficiles à extraire et le mouvement joue un rôle clef dans la caractérisation de l’action.Dans cette thèse, nous introduisons un réseau de neurones spatio-temporel convolutif avec une architecture Jumelle. Ce réseau d'apprentissage profond prend comme entrées une séquence d'images RVB et son flot optique estimé. Les données RVB permettent à notre modèle de capturer les caractéristiques d'apparence tandis que le flot optique capture les caractéristiques de mouvement. Ces deux flux sont traités en parallèle à l'aide de convolutions 3D, et sont fusionnés à la dernière étape du réseau. Les caractéristiques spatio-temporelles extraites dans le réseau permettent une classification efficace des clips vidéo de TTStroke-21. Notre méthode obtient une performance de classification de 93.2% sur l'ensemble des données tests. Appliquée à la tâche jointe de détection et de classification, notre méthode atteint une précision de 82.6%.Nous étudions les performances en fonction des types de données utilisés en entrée et la manière de les fusionner. Différents estimateurs de flot optique ainsi que leur normalisation sont testés afin d’améliorer la précision. Les caractéristiques de chaque branche de notre architecture sont également analysées afin de comprendre le chemin de décision de notre modèle. Enfin, nous introduisons un mécanisme d'attention pour aider le modèle à se concentrer sur des caractéristiques discriminantes et aussi pour accélérer le processus d’entraînement. Nous comparons notre modèle avec d'autres méthodes sur TTStroke-21 et le testons sur d'autres ensembles de données. Nous constatons que les modèles fonctionnant bien sur des bases de données d’actions classiques ne fonctionnent pas toujours aussi bien sur notre base de données d'actions similaires.Les travaux présentés dans cette thèse ont été validés par des publications dans une revue internationale, cinq papiers de conférences internationales, deux papiers d’un workshop international et une tâche reconductible dans le workshop MediaEval où les participants peuvent appliquer leurs méthodes de reconnaissance d'actions à notre base de données TTStroke-21. Deux autres papiers de workshop internationaux sont en cours de préparation, ainsi qu'un chapitre de livre
Action recognition in videos is one of the key problems in visual data interpretation. Despite intensive research, differencing and recognizing similar actions remains a challenge. This thesis deals with fine-grained classification of sport gestures from videos, with an application to table tennis.In this manuscript, we propose a method based on deep learning for automatically segmenting and classifying table tennis strokes in videos. Our aim is to design a smart system for students and teachers for analyzing their performances. By profiling the players, a teacher can therefore tailor the training sessions more efficiently in order to improve their skills. Players can also have an instant feedback on their performances.For developing such a system with fine-grained classification, a very specific dataset is needed to supervise the learning process. To that aim, we built the “TTStroke-21” dataset, which is composed of 20 stroke classes plus a rejection class. The TTStroke-21 dataset comprises video clips of recorded table tennis exercises performed by students at the sport faculty of the University of Bordeaux - STAPS. These recorded sessions were annotated by professional players or teachers using a crowdsourced annotation platform. The annotations consist in a description of the handedness of the player and information for each stroke performed (starting and ending frames, class of the stroke).Fine-grained action recognition has some notable differences with coarse-grained action recognition. In general, datasets used for coarse-grained action recognition, the background context often provides discriminative information that methods can use to classify the action, rather than focusing on the action itself. In fine-grained classification, where the inter-class similarity is high, discriminative visual features are harder to extract and the motion plays a key role for characterizing an action.In this thesis, we introduce a Twin Spatio-Temporal Convolutional Neural Network. This deep learning network takes as inputs an RGB image sequence and its computed Optical Flow. The RGB image sequence allows our model to capture appearance features while the optical flow captures motion features. Those two streams are processed in parallel using 3D convolutions, and fused at the last stage of the network. Spatio-temporal features extracted in the network allow efficient classification of video clips from TTStroke-21. Our method gets an average classification performance of 87.3% with a best run of 93.2% accuracy on the test set. When applied on joint detection and classification task, the proposed method reaches an accuracy of 82.6%.A systematic study of the influence of each stream and fusion types on classification accuracy has been performed, giving clues on how to obtain the best performances. A comparison of different optical flow methods and the role of their normalization on the classification score is also done. The extracted features are also analyzed by back-tracing strong features from the last convolutional layer to understand the decision path of the trained model. Finally, we introduce an attention mechanism to help the model focusing on particular characteristic features and also to speed up the training process. For comparison purposes, we provide performances of other methods on TTStroke-21 and test our model on other datasets. We notice that models performing well on coarse-grained action datasets do not always perform well on our fine-grained action dataset.The research presented in this manuscript was validated with publications in one international journal, five international conference papers, two international workshop papers and a reconductible task in MediaEval workshop in which participants can apply their action recognition methods to TTStroke-21. Two additional international workshop papers are in process along with one book chapter
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Křížová, Martina. "Indexování dat pohybujících se objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237256.

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This thesis deals with indexing of spatio-temporal data. It describes existing approaches to indexing data and support for indexing in Oracle Database 11g. The aim of this work is to design structures of databases for storing spatio-temporal data over Oracle Database 11g to propose experiments for these databases. Ways of spatio-temporal data storage are evaluated according to these experiments in terms of time demands of queries and appropriateness of using available indexing structure and spatial operators.
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Book chapters on the topic "Spatio-temporal indexing"

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Shekhar, Shashi, and Hui Xiong. "Spatio-temporal Indexing." In Encyclopedia of GIS, 1121. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1321.

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Hadjieleftheriou, Marios, George Kollios, Vassilis J. Tsotras, and Dimitrios Gunopulos. "Indexing Spatio-temporal Archives." In Encyclopedia of GIS, 530–38. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_617.

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Hadjieleftheriou, Marios, George Kollios, Vassilis J. Tsotras, and Dimitrios Gunopulos. "Indexing Spatio-temporal Archives." In Encyclopedia of GIS, 1–12. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_617-2.

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Shekhar, Shashi, and Hui Xiong. "Indexing API, Spatial/Spatio-temporal." In Encyclopedia of GIS, 497. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_599.

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Shekhar, Shashi, and Hui Xiong. "Indexing Framework, Spatial/Spatio-temporal." In Encyclopedia of GIS, 502. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_601.

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Mokbel, Mohamed F., and Walid G. Aref. "Indexing Historical Spatio-temporal Data." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_198-2.

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Mokbel, Mohamed F., and Walid G. Aref. "Indexing Historical Spatio-Temporal Data." In Encyclopedia of Database Systems, 1448–51. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_198.

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Šaltenis, Simonas, and Christian S. Jensen. "Indexing of Objects on the Move." In Mining Spatio-Temporal Information Systems, 21–41. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1149-6_2.

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Hausser, Roland. "Spatio-temporal Indexing in Database Semantics." In Computational Linguistics and Intelligent Text Processing, 53–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44686-9_5.

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Barceló, Lluis, Xavier Orriols, and Xavier Binefa. "Spatio-Temporal Decomposition of Sport Events for Video Indexing." In Lecture Notes in Computer Science, 435–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_43.

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Conference papers on the topic "Spatio-temporal indexing"

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Emrich, Tobias, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, and Andreas Züfle. "Indexing uncertain spatio-temporal data." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2396813.

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Theodoridis, Yannis, Michael Vazirgiannis, and Timos Sellis. "Spatio-temporal indexing for large multimedia applications." In Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems. IEEE, 1996. http://dx.doi.org/10.1109/mmcs.1996.535011.

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Cai, Yuhan, and Raymond Ng. "Indexing spatio-temporal trajectories with Chebyshev polynomials." In the 2004 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1007568.1007636.

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Fox, Anthony, Chris Eichelberger, James Hughes, and Skylar Lyon. "Spatio-temporal indexing in non-relational distributed databases." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691586.

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Zhang, Chong, Xiaoying Chen, Bin Ge, and Weidong Xiao. "Indexing historical spatio-temporal data in the cloud." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363948.

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Rodriguez, Felix R., and Manuel Barrena. "Spatio-temporal indexing of the Quikscat wind data." In 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009). IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418129.

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Piro, Paolo, Sandrine Anthoine, Eric Debreuve, and Michel Barlaud. "Scalable Spatio-Temporal Video Indexing Using Sparse Multiscale Patches." In 2009 Seventh International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2009. http://dx.doi.org/10.1109/cbmi.2009.48.

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Sameh, Megrhi, Souidene Wided, Azeddine Beghdadi, and Chokri B. Amar. "Video indexing using salient region based spatio-temporal segmentation approach." In 2012 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2012. http://dx.doi.org/10.1109/icmcs.2012.6320204.

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"An Efficient Strategy for Spatio-temporal Data Indexing and Retrieval." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004137102270232.

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Martin, Pierre-Etienne, Jenny Benois-Pineau, Renaud Peteri, and Julien Morlier. "Sport Action Recognition with Siamese Spatio-Temporal CNNs: Application to Table Tennis." In 2018 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2018. http://dx.doi.org/10.1109/cbmi.2018.8516488.

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