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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/27| Real-time human action recognition using a reduced feature set | |
| GLORIA CASTRO MUNOZ | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Real-time Human action recognition Video surveillance Support Vector Machine | |
| The Human Action Recognition (HAR) from video sequences is a topic which has captured the interest of a large number of researchers from industry, academia, consumer agencies and security agencies. The solid interest in the topic is motivated by the wide variety and importance of promising applications for example rehabilitation of patients, monitoring and supporting of children and elderly people, automatic annotation of video, human-computer interfaces, and video surveillance among others. Particularly the increasing demand for security and safety by society in recent years has occasioned significant advances in video surveillance technology. However despite these advances, video surveillance systems are not able to analyze in realtime the huge amounts of video coming from video surveillance cameras installed in the worldwide and therefore, they can’t detect and alert about potential criminal activity in real-time. | |
| Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| 11-12-2015 | |
| Tesis de doctorado | |
| Inglés | |
| Público en general | |
| Castro-Muñoz G. | |
| ELECTRÓNICA | |
| Aparece en las colecciones: | Doctorado en Electrónica |
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| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| CastroMuG.pdf | 2.85 MB | Adobe PDF | Visualizar/Abrir |