Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102292
Title: Human action recognition with video data : research and evaluation challenges
Authors: Ramanathan, Manoj
Yau, Wei-Yun
Teoh, Eam Khwang
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2014
Source: Ramanathan, M., Yau, W.-Y., & Teoh, E. K. (2014). Human action recognition with video data : research and evaluation challenges, 44(5), 650 - 663.
Series/Report no.: IEEE transactions on human-machine systems
Abstract: Given a video sequence, the task of action recognition is to identify the most similar action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has wide application including surveillance, video indexing, biometrics, telehealth and human computer interaction. Vision-based human action recognition is affected by several challenges due to view changes, occlusion, variation in execution rate, anthropometry, camera motion and background clutter. In this survey, we provide an overview of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification will help researchers to identify the suitable methods available to address each of the challenges faced and their limitations. In addition, we also identify the publicly available datasets and the challenges posed by them. From this survey, we draw conclusions regarding how well a challenge has been solved and we identify potential research areas that require further work.
URI: https://hdl.handle.net/10356/102292
http://hdl.handle.net/10220/24233
DOI: http://dx.doi.org/10.1109/THMS.2014.2325871
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/THMS.2014.2325871].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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