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dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAk, Duygu Selin
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2021-04-16T07:46:36Z
dc.date.available2021-04-16T07:46:36Z
dc.date.issued2021en_US
dc.identifier.citationÖzyer, T., Ak, D. S. ve Alhajj, R. (2021). Human action recognition approaches with video datasets-A survey. Knowledge-Based Systems, 222. https://dx.doi.org/10.1016/j.knosys.2021.106995en_US
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.urihttps://dx.doi.org/10.1016/j.knosys.2021.106995
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6734
dc.description.abstractHuman Activity Recognition has recently attracted considerable attention. This has been triggered by the rapid development of advance technologies and learning methods. Human action recognition can be actively used in a number of application domains which may positively influence various aspects of the daily life. These include, (1) preventing dangerous activities and detection of crimes such as theft, murder, and property damage, and (2) predicting pedestrian activities in traffic, among others. To better serve these applications and the like, it is essential to highlight the various aspects related to the existing methods so that their actual users could realize and identify the good performing methods that work fast and are capable of recognizing the correct activities with high accuracy. The latter scope is covered in this survey which summarizes and analyzes the methods that perform learning and analysis processes on video datasets to grasp a new perspective on human action recognition. The survey also covers the major datasets commonly used in human activity recognition research. Accordingly, this survey could be recognized as a valuable source for researchers and practitioners.en_US
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectDangerous Activity Recognitionen_US
dc.subjectHuman Activity Recognitionen_US
dc.subjectVideo Analysisen_US
dc.titleHuman action recognition approaches with video datasets-A surveyen_US
dc.typearticleen_US
dc.relation.journalKnowledge-Based Systemsen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authorid0000-0001-6657-9738en_US
dc.identifier.volume222en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.knosys.2021.106995en_US


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