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A Review on Human Activity Recognition Using Vision-Based Method.
Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen.
Afiliación
  • Zhang S; College of Information Science and Engineering, Ocean University of China, Qingdao, China.
  • Wei Z; College of Information Science and Engineering, Ocean University of China, Qingdao, China.
  • Nie J; Department of Computer Science and Technology, Tsinghua University, Beijing, China.
  • Huang L; College of Information Science and Engineering, Ocean University of China, Qingdao, China.
  • Wang S; College of Information Science and Engineering, Ocean University of China, Qingdao, China.
  • Li Z; College of Information Science and Engineering, Ocean University of China, Qingdao, China.
J Healthc Eng ; 2017: 3090343, 2017.
Article en En | MEDLINE | ID: mdl-29065585
Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Visión Ocular / Reconocimiento de Normas Patrones Automatizadas / Actividades Humanas Límite: Humans Idioma: En Revista: J Healthc Eng Año: 2017 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Visión Ocular / Reconocimiento de Normas Patrones Automatizadas / Actividades Humanas Límite: Humans Idioma: En Revista: J Healthc Eng Año: 2017 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido