Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6835
Title: Human action recognition approaches with video datasets-A survey
Authors: Ozyer, Tansel
Ak, Duygu Selin
Alhajj, Reda
Keywords: Human activity recognition
Video analysis
Dangerous activity recognition
Issue Date: 2021
Publisher: Elsevier
Abstract: Human 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. (C) 2021 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.knosys.2021.106995
https://hdl.handle.net/20.500.11851/6835
ISSN: 0950-7051
1872-7409
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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