23 research outputs found
Action Recognition in Tennis Videos using Optical Flow and Conditional Random Fields
The aim of Action Recognition is the automated analysis and interpretation of events in video sequences. As result of the applications that can be developed, and the widespread availability and popularization of digital video (security cameras, monitoring, social networks, among many other), this area is currently the focus of a strong and wide research interest in various domains such as video security, humancomputer interaction, patient monitoring and video retrieval, among others.
Our long-term goal is to develop automatic action identification in video sequences using Conditional Random Fields (CRFs). In this work we focus, as a case of study, in the identification of a limited set of tennis shots during tennis matches. Three challenges have been addressed: player tracking, player movements representation and action recognition.
Video processing techniques are used to generate textual tags in specific frames, and then the CRFs are used as a classifier to recognise the actions performed in those frames. The preliminary results appear to be quite promising.Sociedad Argentina de Inform谩tica e Investigaci贸n Operativ
Action Recognition in Tennis Videos using Optical Flow and Conditional Random Fields
The aim of Action Recognition is the automated analysis and interpretation of events in video sequences. As result of the applications that can be developed, and the widespread availability and popularization of digital video (security cameras, monitoring, social networks, among many other), this area is currently the focus of a strong and wide research interest in various domains such as video security, humancomputer interaction, patient monitoring and video retrieval, among others.
Our long-term goal is to develop automatic action identification in video sequences using Conditional Random Fields (CRFs). In this work we focus, as a case of study, in the identification of a limited set of tennis shots during tennis matches. Three challenges have been addressed: player tracking, player movements representation and action recognition.
Video processing techniques are used to generate textual tags in specific frames, and then the CRFs are used as a classifier to recognise the actions performed in those frames. The preliminary results appear to be quite promising.Sociedad Argentina de Inform谩tica e Investigaci贸n Operativ
Adquisici贸n y procesamiento de im谩genes a茅reas para sensado remoto
Los sistemas de adquisici贸n de im谩genes a茅reas digitales de alta resoluci贸n basados en c谩maras de formato peque帽o representan una alternativa de gran versatilidad en la soluci贸n de diversos problemas de monitoreo ambiental y sensado remoto, particularmente aquellas obtenidas en base a dispositivos a茅reos aut贸nomos UAV. Esto es a铆 dado que un correcto procesamiento de im谩genes de c谩maras de formato peque帽o (贸pticas y/o mulitespectrales) permite la obtenci贸n de ortomosaicos que cumplan con est谩ndares de calidad, a una fracci贸n del costo operativo del uso de equipos aerotransportados de costo mucho mayor, y con mayor flexibilidad y resoluci贸n que con el uso de im谩genes satelitales. El objetivo de este trabajo de investigaci贸n es la implementaci贸n de un sistema de computo que permita sistematizar la adquisici贸n, procesamiento, rectificaci贸n, formaci贸n de mosaicos, y georreferenciaci贸n de las im谩genes adquiridas por medio de c谩maras 贸pticas y multiespectrales transportadas por un UAV.
Los resultados obtenidos permiten automatizar el procesamiento casi por completo, requiriendo un m铆nimo de atenci贸n no especializada, y permiten organizar la informaci贸n e im谩genes obtenidas en los vuelos para su posterior uso en procesos de reconocimiento, interpretaci贸n e identificaci贸n.VII Workshop Computaci贸n Gr谩fica, Im谩genes y Visualizaci贸n (WCGIV)Red de Universidades con Carreras en Inform谩tica (RedUNCI
Adquisici贸n y procesamiento de im谩genes a茅reas para sensado remoto
Los sistemas de adquisici贸n de im谩genes a茅reas digitales de alta resoluci贸n basados en c谩maras de formato peque帽o representan una alternativa de gran versatilidad en la soluci贸n de diversos problemas de monitoreo ambiental y sensado remoto, particularmente aquellas obtenidas en base a dispositivos a茅reos aut贸nomos UAV. Esto es a铆 dado que un correcto procesamiento de im谩genes de c谩maras de formato peque帽o (贸pticas y/o mulitespectrales) permite la obtenci贸n de ortomosaicos que cumplan con est谩ndares de calidad, a una fracci贸n del costo operativo del uso de equipos aerotransportados de costo mucho mayor, y con mayor flexibilidad y resoluci贸n que con el uso de im谩genes satelitales. El objetivo de este trabajo de investigaci贸n es la implementaci贸n de un sistema de computo que permita sistematizar la adquisici贸n, procesamiento, rectificaci贸n, formaci贸n de mosaicos, y georreferenciaci贸n de las im谩genes adquiridas por medio de c谩maras 贸pticas y multiespectrales transportadas por un UAV.
Los resultados obtenidos permiten automatizar el procesamiento casi por completo, requiriendo un m铆nimo de atenci贸n no especializada, y permiten organizar la informaci贸n e im谩genes obtenidas en los vuelos para su posterior uso en procesos de reconocimiento, interpretaci贸n e identificaci贸n.VII Workshop Computaci贸n Gr谩fica, Im谩genes y Visualizaci贸n (WCGIV)Red de Universidades con Carreras en Inform谩tica (RedUNCI
Aerial image acquisition and processing for remote sensing
UAV (Unmanned Airborne Vehicle) high resolution digital image acquisition systems based on small format cameras provide a versatile alternative to the solution of environmental monitoring and remote sensing problems. Through digital image processing these small format optical and multi-spectral camera images can obtain orthomosaics that meet quality standards at a fraction of the cost of traditional heavier manned vehicle equipment. They also have higher availability, resolution and flexibility can also be obtained when compared to satellite images. This paper presents research and development undertaken to produce a computational system that can automatically process optical and multi-spectral images obtained from digital cameras mounted on a UAV aircraft. The system acquires, rectifies, mosaics and georeferences these images with minimum operator assistance. Results prove that the process can almost be fully automated and that the system can be operated by minimally trained personnel. Processed images obtained by the software can be used for pattern recognition, photo interpretation, photogrammetry, and other remote sensing applications.Facultad de Inform谩tic
Reconocimiento de Acciones en videos de tenis usando Flujo Optico y CRF
El objetivo del Reconocimiento de Acciones (Action Recognition) es el an谩lisis e interpretaci贸n automatizados de eventos particulares en secuencias de video. Esta 谩rea est谩 siendo 谩mpliamente investigada en diferentes dominios tales como videos de seguridad, interacci贸n humano-computadora, monitoreo de pacientes y recuperaci贸n de video, entre otros, dadas las importantes aplicaciones que pueden desarrollarse, y la proliferaci贸n de c谩maras y videos de seguridad y monitoreo en la actualidad.
El objetivo de este proyecto es la identificaci贸n autom谩tica de acciones en secuencia de videos, utilizando Conditional Random Fields (CRFs). Como caso de estudio se utilizan videos de partidos de tenis para la identificaci贸n de golpes. Se abordan tres desaf铆os, el tracking, la representaci贸n del movimiento del jugador y el reconocimiento de acciones.Eje: Computaci贸n Gr谩fica, Im谩genes y Visualizaci贸nRed de Universidades con Carreras en Inform谩tica (RedUNCI
Action Recognition in Tennis Videos using Optical Flow and Conditional Random Fields
The aim of Action Recognition is the automated analysis and interpretation of events in video sequences. As result of the applications that can be developed, and the widespread availability and popularization of digital video (security cameras, monitoring, social networks, among many other), this area is currently the focus of a strong and wide research interest in various domains such as video security, humancomputer interaction, patient monitoring and video retrieval, among others.
Our long-term goal is to develop automatic action identification in video sequences using Conditional Random Fields (CRFs). In this work we focus, as a case of study, in the identification of a limited set of tennis shots during tennis matches. Three challenges have been addressed: player tracking, player movements representation and action recognition.
Video processing techniques are used to generate textual tags in specific frames, and then the CRFs are used as a classifier to recognise the actions performed in those frames. The preliminary results appear to be quite promising.Sociedad Argentina de Inform谩tica e Investigaci贸n Operativ
The Quaternary Active Faults Database of Iberia (QAFI v.2.0)
The Quaternary Active Faults Database of Iberia (QAFI) is an initiative lead by the Institute of Geology and Mines of Spain (IGME) for building a public repository of scientific data regarding faults having documented activity during the last 2.59 Ma (Quaternary). QAFI also addresses a need to transfer geologic knowledge to practitioners of seismic hazard and risk in Iberia by identifying and characterizing seismogenic fault-sources. QAFI is populated by the information freely provided by more than 40 Earth science researchers, storing to date a total of 262 records. In this article we describe the development and evolution of the database, as well as its internal architecture. Aditionally, a first global analysis of the data is provided with a special focus on length and slip-rate fault parameters. Finally, the database completeness and the internal consistency of the data are discussed. Even though QAFI v.2.0 is the most current resource for calculating fault-related seismic hazard in Iberia, the database is still incomplete and requires further review