Relevant bibliographies by topics / Spatio-temporal trajectories

Academic literature on the topic 'Spatio-temporal trajectories'

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Published: 25 May 2024

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Journal articles on the topic "Spatio-temporal trajectories"

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Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing spatio-temporal trajectories." Computational Geometry 42, no. 9 (November 2009): 825–41. http://dx.doi.org/10.1016/j.comgeo.2009.02.002.

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Bao, Wei, Li Xin Ji, Shi Lin Gao, Xing Li, and Li Xiong Liu. "Video Copy Detection Based on Fusion of Spatio-Temporal Features." Applied Mechanics and Materials 347-350 (August 2013): 3653–61. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3653.

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A video copy detection method based on fusion of spatio-temporal features is proposed in this paper. Firstly, trajectories are built and lens boundaries are detected by SURF features analyzing, then normalized histogram is used to describe spatio-temporal behavior of trajectories, the bag of visual words is constructed by trajectories behavior clustering, word frequency vectors and SURF features with behavior labels are extracted to express spatio-temporal content of lens, finally, duplicates are detected efficiently based on grade-match. The experimental results show the performance of this method is improved greatly compared with other similar methods.
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Zhang, Ran, Xiaohui Chen, Lin Ye, Wentao Yu, Bing Zhang, and Junnan Liu. "Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix." Applied Sciences 14, no. 10 (May 12, 2024): 4104. http://dx.doi.org/10.3390/app14104104.

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This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.
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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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Sandu Popa, Iulian, Karine Zeitouni, Vincent Oria, and Ahmed Kharrat. "Spatio-temporal compression of trajectories in road networks." GeoInformatica 19, no. 1 (May 3, 2014): 117–45. http://dx.doi.org/10.1007/s10707-014-0208-4.

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Zhang, Dongzhi, Kyungmi Lee, and Ickjai Lee. "Semantic periodic pattern mining from spatio-temporal trajectories." Information Sciences 502 (October 2019): 164–89. http://dx.doi.org/10.1016/j.ins.2019.06.035.

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Wang, Jiang, Cheng Zhu, Yun Zhou, and Weiming Zhang. "Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering." Journal of Navigation 70, no. 6 (July 3, 2017): 1383–400. http://dx.doi.org/10.1017/s0373463317000406.

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Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.
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Arslan, Muhammad, Christophe Cruz, Ana-Maria Roxin, and Dominique Ginhac. "Spatio-temporal analysis of trajectories for safer construction sites." Smart and Sustainable Built Environment 7, no. 1 (April 3, 2018): 80–100. http://dx.doi.org/10.1108/sasbe-10-2017-0047.

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Purpose The purpose of this paper is to improve the safety of construction workers by understanding their behaviors on construction sites using spatio-temporal (ST) trajectories. Design/methodology/approach A review of construction safety management literature and international occupational health and safety statistics shows that the major reasons for fatalities on construction sites are mobility-related issues, such as unsafe human behaviors, difficult site conditions, and workers falling from heights and striking against or being struck by moving objects. Consequently, literature has been reviewed to find possible technological solutions to track the mobility of construction workers to reduce fatalities. This examination has suggested that location acquisition systems, such as Global Positioning System (GPS), have been widely used for real-time monitoring and tracking of workers on construction sites for hazard prevention. However, the raw data captured from GPS devices are generally available as discrete points and do not hold enough information to understand the workers’ mobility. As a solution, an application to transform raw GPS data into ST trajectories using different preprocessing algorithms is proposed for enhancing worker safety on construction sites. Findings The proposed system preprocesses raw GPS data for stay point detection, trajectory segmentation and intersection of multiple trajectories to find significant places and movements of workers on a construction site to enhance the information available to H&S managers for decision-making processes. In addition, it reduces the size of trajectory data for future analyses. Originality/value Application of location acquisition systems for construction safety management is very well addressed in the existing literature. However, a significant gap has been found: the usage of preprocessed ST trajectories is still missing in workers’ safety monitoring scenarios in the area of construction management. To address this research gap, the proposed system uses preprocessed ST trajectories to monitor workers’ movements on a construction site to identify potentially unsafe behaviors.
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Zhang, Chengcui. "A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining." International Journal of Multimedia Data Engineering and Management 4, no. 3 (July 2013): 42–60. http://dx.doi.org/10.4018/jmdem.2013070103.

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The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.
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Boulmakoul, Azedine. "Moving Object Trajectories Meta-Model and Spatio-Temporal Queries." International Journal of Database Management Systems 4, no. 2 (April 30, 2012): 35–54. http://dx.doi.org/10.5121/ijdms.2012.4203.

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

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Ishikawa, Yoshiharu. "SPATIO-TEMPORAL DATA MINING FROM MOVING OBJECT TRAJECTORIES." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10446.

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Partsinevelos, Panayotis. "Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/PartsinevelosP2002.pdf.

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Jin, Meihan. "Un modèle spatio-temporel sémantique pour la modélisation de mobilités en milieu urbain." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0067/document.

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La croissance rapide et la complexité de nombreuses villes contemporaines offrent de nombreux défis de recherche pour les scientifiques à la recherche d'une meilleure compréhension des mobilités qui se produisent dans l'espace et dans le temps. A l’heure où de très grandes séries de données de trajectoires en milieu urbain sont disponibles grâce à profusion de nombreux capteurs de positionnement et de services de nombreuses et nouvelles opportunités de recherche et d’application nous sont offertes. Cependant, une bonne intégration de ces données de mobilité nécessite encore l'élaboration de cadres méthodologiques et conceptuels tout comme la mise en oeuvre de bases de données spatio-temporelles qui offriront les capacités appropriées de représentation et de manipulation des données. La recherche développée dans cette thèse introduit une modélisation conceptuelle et une approche de gestion de base de données spatio-temporelles pour représenter et analyser des trajectoires humaines dans des espaces urbains. Le modèle considère les dimensions spatiales, temporelles et sémantiques afin de tenir compte de l’ensemble des propriétés issues des informations de mobilité. Plusieurs abstractions de données de mobilité et des outils de manipulation de données sont développés et expérimentés à partir d’une large base de données de trajectoires disponibles dans la ville de Pékin. L'intérêt de l'approche est double: il montre d’une part que de larges ensembles de données de mobilité peuvent être intégrés au sein de SGBD spatiotemporels extensibles; d’autre part des outils de manipulation et d’interrogation spécifiques peuvent être dérivés à partir de fonctions intégrées au sein d’un langage d’interrogation. Le potentiel de l’approche est illustré par une série d’interrogations qui montrent comment à partir d’une large base de données de trajectoires quelques patrons de déplacements peuvent être obtenus
Massive trajectory datasets generated in modern cities generate not only novel research opportunities but also important methodological challenges for academics and decision-makers searching for a better understanding of travel patterns in space and time. This PhD research is oriented towards the conceptual and GIS-based modeling of human displacements derived from large sets of urban trajectories. The motivation behind this study originates from the necessity to search for and explore travel patterns that emerge from citizens acting in the city. Our research introduces a conceptual modelling framework whose objective is to integrate and analyze human displacements within a GIS-based practical solution. The framework combines conceptual and logical models that represent travel trajectories of citizens moving in a given city. The whole approach has been implemented in a geographical database system, experimented in the context of transportation data, and enriched by a series of query interface manipulations and specific functions that illustrate the potential of our whole framework for urban studies. The whole framework has been experimented on top of the Geolife project and large trajectories datasets available in the city of Beijing. Overall, the findings are twofold: first, it appears that our modelling framework can appropriately act as an extensible geographical database support for the integration of large trajectory datasets; second the approach shows that several emerging human displacements can be explored from the manipulation of large urban trajectories
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Palma, Andrey Luis Tietbohl. "A clustering-based approach for discovering interesting places in trajectories." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/17024.

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Por causa da grande quantidade de dados de trajetórias producidos por dispositivos móveis, existe um aumento crescente das necessidades de mecanismos para extrair conhecimento a partir desses dados. A maioria dos trabalhos existentes focam nas propriedades geometricas das trajetorias, mas recentemente surgiu o conceito de trajetórias semânticas, nas quais a informação da geografia por baixo da trajetória é integrada aos pontos da trajetória. Nesse novo conceito, trajetórias são observadas como um conjunto de stops e moves, onde stops são as partes mais importantes da trajetória. Os stops e moves são computados pela intersecção das trajetórias com o conjunto de objetos geográficos dados pelo usuário. Nessa dissertação será apresentada uma solução alternativa a descoberta de stops, com a capacidade de achar lugares de interesse que não são esperados pelo usuário. A solução proposta é um método de clusterização espaço-temporal, baseado na velocidade, para ser aplicado em uma trajetória. Foram comparadas duas abordagens diferentes com experimentos baseados em dados reais e mostrado que a computação de stops usando o conceito de velocidade pode ser interessante para várias applicações.
Because of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have focused on the geometric properties of trajectories, but recently emerged the concepts of semantic trajectories, in which the background geographic information is integrated to trajectory sample points. In this new concept, trajectories are observed as a set of stops and moves, where stops are the most important parts of the trajectory. Stops and moves have been computed by testing the intersection of trajectories with a set of geographic objects given by the user. In this dissertation we present an alternative solution with the capability of finding interesting places that are not expected by the user. The proposed solution is a spatio-temporal clustering method, based on speed, to work with single trajectories. We compare the two different approaches with experiments on real data and show that the computation of stops using the concept of speed can be interesting for several applications.
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Wu, Jing. "A qualitative spatio-temporal modelling and reasoning approach for the representation of moving entities." Thesis, Brest, 2015. http://www.theses.fr/2015BRES0036/document.

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La recherche développée dans cette thèse introduit une approche qualitative pour représenter et raisonner à partir d'entités spatiales dans un espace géographique à deux dimensions. Les patrons de mouvements entre entités dynamiques sont catégorisés à partir d'un modèle qualitatif de relations topologiques entre une ligne orientée et une région, et de relations d'orientation entre deux lignes orientées, respectivement. Les mouvements qualitatifs sont dérivés à partir de relations spatio-temporelles qui caractérisent des entités dynamiques conceptualisées comme des points ou des régions dans un espace à deux dimensions. Cette architecture de raisonnement permet de dériver des configurations de mouvements basiques dérivées à partir d'entités statiques et dynamiques. L'approche est complétée par une qualification de ces configurations à partir d'expressions du langage naturel. Les compositions de mouvements sont étudiées tout comme les transitions possibles dans des cas de données incomplètes. Les tables de compositions sont également explorées et permettent d'étendre les possibilités de raisonnement. Le modèle est expérimenté dans le contexte de l'analyse de trajectoires aériennes et maritimes
The research developed in this thesis introduces a qualitative approach for representing and reasoning on moving entities in a two-dimensional geographical space. Movement patterns of moving entities are categorized based on a series of qualitative spatial models of topological relations between a directed line and a region, and orientation relations between two directed lines, respectively. Qualitative movements are derived from the spatio-temporal relations that characterize moving entities conceptualized as either points or regions in a two-dimensional space. Such a spatio-temporal framework supports the derivation of the basic movement configurations inferred from moving and static entities. The approach is complemented by a tentative qualification of the possible natural language expressions of the primitive movements identified. Complex movements can be represented by a composition of these primitive movements. The notion of conceptual transition that favors the exploration of possible trajectories in the case of incomplete knowledge configurations is introduced and explored.Composition tables are also studied and provide additional reasoning capabilities. The whole approach is applied to the analysis of flight patterns and maritime trajectories
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Vercelloni, Julie. "Quantifying the state of populations and effects of disturbances at large spatio-temporal scales: The case of coral populations in the great barrier reef." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/87812/1/Julie_Vercelloni_Thesis.pdf.

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This project was a step forward in applying statistical methods and models to provide new insights for more informed decision-making at large spatial scales. The model has been designed to address complicated effects of ecological processes that govern the state of populations and uncertainties inherent in large spatio-temporal datasets. Specifically, the thesis contributes to better understanding and management of the Great Barrier Reef.
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Reux, Sara. "Les figures de la discontinuité dans le développement résidentiel périurbain : application à la région Limousin." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0019/document.

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Alors que la continuité du bâti ne suffit plus pour appréhender l’espace urbain d’aujourd’hui,la discontinuité du tissu urbain est devenue une clé de compréhension de la ville contemporaine et de sonprocessus de formation. Elle suscite l'intérêt des chercheurs, d'autant plus que le déploiement des systèmesd'information géographique offre de nouvelles perspectives de mesure des formes urbaines. Mais, si lestravaux en écologie du paysage ou en géographie permettent de mesurer l'émergence de ces formesdiscontinues, il nous semble important de nous intéresser aux fondements économiques de l'urbanisationdiscontinue qui commencent à faire l’objet de travaux empiriques en économie. La constitution d’une grillede lecture de l’urbanisation discontinue nous permet de comprendre de manière concomitante la formationdes espaces périurbains et les formes de développement de l’habitat à l’échelle parcellaire. Cette rechercheest appliquée au Limousin sur la période 1950-2009. Le prisme de la discontinuité nous apporte un éclairagesur les trajectoires de développement résidentiel des communes de cette région. La construction d’une basede données spatio-temporelles nous offre la possibilité de lire ces trajectoires à partir de l’association demesures de dispersion géographique et de dispersion morphologique de l’habitat. À partir de ces mesuresde dispersion, nous abordons l’articulation des logiques fonctionnelles et morphologiques du développementrésidentiel grâce à la construction d’une base de données multithématiques. Pour comprendre les schémasde localisation des ménages, nous analysons plus particulièrement les problématiques de la production deslogements, de l’interaction entre structure foncière et régulation publique à l’échelle des communes et del’influence des aménités et désaménités des espaces urbains et ruraux sur la dispersion de l’habitat
While understanding urban areas through continuity of developed land reached its limits,discontinuity of urban fabrics has become a key to understand today's cities and their shaping dynamics. Itraises researchers’ interest especially as GIS development gives new opportunities to measure urbanpatterns. While researches in landscape ecology or geography allow to measure discontinuous patterns, itseems to be important to focus on their economic foundations which are a matter for recent empiricalresearches in economy. The construction of an analytical grid of discontinuous urban patterns allows tounderstand simultaneously peri-urban development and patterns of residential development at the parcellevel. This research is applied to the Limousin region on the 1950-2009 period. The focus on discontinuousurban patterns sheds light on residential trajectories of the Limousin region's communes. The proposal of aspatio-temporal data base allows to understand these trajectories through combined measures of geographical dispersion and morphological dispersion. With these measures, we broach the link betweenfunctional and morphological dynamics thanks to a multitheme data base. To understand household locationand residential dispersion, we analyze the issue of housing production, the interaction between property andpublic regulation at the scale of communes, the influence of amenities and desamenities of urban and ruralspaces
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Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont proposées : Une méthode d'analyse des trajectoires, basée sur les concepts formels fréquents, est utilisée pour détecter les différents comportements d’évolution de trajectoires dans le temps. Ces comportements sont “latents”, "emerging", "decreasing", "lost" et "jumping". Ils caractérisent la dynamique de la mobilité par rapport à l'espace urbain et le temps. Les comportements détectés sont visualisés sur des cartes générées automatiquement à différents niveaux spatio-temporels pour affiner l'analyse de la mobilité dans une zone donnée de la ville. Une deuxième méthode basée sur l'extraction de concepts formels séquentiels fréquents a également été proposée pour exploiter la direction des mouvements dans la détection de l'évolution. Enfin, une méthode de prédiction basée sur les chaînes de Markov est présentée pour prévoir le comportement d’évolution dans la future période pour une région. Ces trois méthodes sont évaluées sur ensembles de données réelles . Les résultats expérimentaux obtenus sur ces données valident la pertinence de la proposition et l'utilité des cartes produites
This dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
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Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont proposées : Une méthode d'analyse des trajectoires, basée sur les concepts formels fréquents, est utilisée pour détecter les différents comportements d’évolution de trajectoires dans le temps. Ces comportements sont “latents”, "emerging", "decreasing", "lost" et "jumping". Ils caractérisent la dynamique de la mobilité par rapport à l'espace urbain et le temps. Les comportements détectés sont visualisés sur des cartes générées automatiquement à différents niveaux spatio-temporels pour affiner l'analyse de la mobilité dans une zone donnée de la ville. Une deuxième méthode basée sur l'extraction de concepts formels séquentiels fréquents a également été proposée pour exploiter la direction des mouvements dans la détection de l'évolution. Enfin, une méthode de prédiction basée sur les chaînes de Markov est présentée pour prévoir le comportement d’évolution dans la future période pour une région. Ces trois méthodes sont évaluées sur ensembles de données réelles . Les résultats expérimentaux obtenus sur ces données valident la pertinence de la proposition et l'utilité des cartes produites
This dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
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Strat, Sabin Tiberius. "Analyse et interprétation de scènes visuelles par approches collaboratives." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00959081.

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Les dernières années, la taille des collections vidéo a connu une forte augmentation. La recherche et la navigation efficaces dans des telles collections demande une indexation avec des termes pertinents, ce qui nous amène au sujet de cette thèse, l'indexation sémantique des vidéos. Dans ce contexte, le modèle Sac de Mots (BoW), utilisant souvent des caractéristiques SIFT ou SURF, donne de bons résultats sur les images statiques. Notre première contribution est d'améliorer les résultats des descripteurs SIFT/SURF BoW sur les vidéos en pré-traitant les vidéos avec un modèle de rétine humaine, ce qui rend les descripteurs SIFT/SURF BoW plus robustes aux dégradations vidéo et qui leurs donne une sensitivité à l'information spatio-temporelle. Notre deuxième contribution est un ensemble de descripteurs BoW basés sur les trajectoires. Ceux-ci apportent une information de mouvement et contribuent vers une description plus riche des vidéos. Notre troisième contribution, motivée par la disponibilité de descripteurs complémentaires, est une fusion tardive qui détermine automatiquement comment combiner un grand ensemble de descripteurs et améliore significativement la précision moyenne des concepts détectés. Toutes ces approches sont validées sur les bases vidéo du challenge TRECVid, dont le but est la détection de concepts sémantiques visuels dans un contenu multimédia très riche et non contrôlé.
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Book chapters on the topic "Spatio-temporal trajectories"

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Frentzos, Elias, Yannis Theodoridis, and Apostolos N. Papadopoulos. "Spatio-Temporal Trajectories." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_364-2.

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Frentzos, Elias, Yannis Theodoridis, and Apostolos N. Papadopoulos. "Spatio-Temporal Trajectories." In Encyclopedia of Database Systems, 2742–46. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_364.

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Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing Spatio-temporal Trajectories." In Algorithms and Computation, 763–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-77120-3_66.

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Padoy, Nicolas, and Gregory D. Hager. "Spatio-Temporal Registration of Multiple Trajectories." In Lecture Notes in Computer Science, 145–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23623-5_19.

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Zhang, Dongzhi, Kyungmi Lee, and Ickjai Lee. "Mining Medical Periodic Patterns from Spatio-Temporal Trajectories." In Health Information Science, 123–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01078-2_11.

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Zhang, Pengdong, Min Deng, and Nico Van de Weghe. "Clustering Spatio-temporal Trajectories Based on Kernel Density Estimation." In Computational Science and Its Applications – ICCSA 2014, 298–311. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09144-0_21.

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Hellbach, Sven, Julian P. Eggert, Edgar Körner, and Horst-Michael Gross. "Basis Decomposition of Motion Trajectories Using Spatio-temporal NMF." In Artificial Neural Networks – ICANN 2009, 804–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_81.

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Hwang, Jung-Rae, Hye-Young Kang, and Ki-Joune Li. "Spatio-temporal Similarity Analysis Between Trajectories on Road Networks." In Perspectives in Conceptual Modeling, 280–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11568346_30.

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Gryllakis, Fragkiskos, Nikos Pelekis, Christos Doulkeridis, Stylianos Sideridis, and Yannis Theodoridis. "Searching for Spatio-Temporal-Keyword Patterns in Semantic Trajectories." In Advances in Intelligent Data Analysis XVI, 112–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68765-0_10.

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Ni, Jinfeng, and Chinya V. Ravishankar. "PA-Tree: A Parametric Indexing Scheme for Spatio-temporal Trajectories." In Advances in Spatial and Temporal Databases, 254–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535331_15.

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Conference papers on the topic "Spatio-temporal trajectories"

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Meskovic, E., D. Osmanovic, Z. Galic, and M. Baranovic. "Generating spatio-temporal streaming trajectories." In 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2014. http://dx.doi.org/10.1109/mipro.2014.6859738.

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Xing, Songhua, Xuan Liu, Qing He, and Arun Hampapur. "Mining Trajectories for Spatio-temporal Analytics." In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.25.

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Patel, Dhaval, Chidansh Bhatt, Wynne Hsu, Mong Li Lee, and Mohan Kankanhalli. "Analyzing Abnormal Events from Spatio-temporal Trajectories." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.45.

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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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Karapiperis, Dimitrios, Aris Gkoulalas-Divanis, and Vassilios S. Verykios. "Linkage of Spatio-Temporal Data and Trajectories." In 2019 IEEE International Smart Cities Conference (ISC2). IEEE, 2019. http://dx.doi.org/10.1109/isc246665.2019.9071724.

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Galasso, Fabio, Masahiro Iwasaki, Kunio Nobori, and Roberto Cipolla. "Spatio-temporal clustering of probabilistic region trajectories." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126438.

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Frihida, Ali, Donia Zheni, Christophe Claramunt, and Henda Ben Ghezala. "Modeling Trajectories: A Spatio-Temporal Data Type Approach." In 2009 20th International Workshop on Database and Expert Systems Application. DEXA 2009. IEEE, 2009. http://dx.doi.org/10.1109/dexa.2009.70.

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Badretdinov, R., E. Takhavova, and M. Shleimovich. "Characteristic Trajectories Detection in Spatio-Temporal Data Streams." In 2019 International Science and Technology Conference "EastConf". IEEE, 2019. http://dx.doi.org/10.1109/eastconf.2019.8725376.

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Brkic, K., S. Segvic, Z. Kalafatic, I. Sikiric, and A. Pinz. "Generative modeling of spatio-temporal traffic sign trajectories." In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2010. http://dx.doi.org/10.1109/cvprw.2010.5543888.

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Olszewska, Joanna Isabelle. "Cylindric Clock Model to Represent Spatio-temporal Trajectories." In 10th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006649605590564.

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