Action Recognition Framework using Saliency Detection and Random Subspace Ensemble Classifier

International Journal of Research and Engineering

View Publication Info
 
 
Field Value
 
Title Action Recognition Framework using Saliency Detection and Random Subspace Ensemble Classifier
 
Creator Zaw, Sai Maung Maung
Aye, Hnin Mya
 
Description Action recognition can be defined as a problem to determine what kind of action is happening in a video. It is a process of matching the observation with the previously labelled samples and assigning label to that observation. In this paper, a framework of the action recognition system based on saliency detection and random subspace ensemble classifier, is introduced in order to increase the performance of the action recognition. The proposed action recognition framework can be partitioned into three main processing phases. The first processing phase is detecting salient foreground objects by considering pattern and color distinctness of a set of pixels in each video frame. In the second processing phase, changing gradient orientation features are used as a useful feature representation. The third processing phase is recognizing actions using random subspace ensemble classifier with discriminant learner. Experimental results are evaluated on the UIUC action dataset. The proposed action recognition framework achieved satisfying action recognition accuracy.
 
Publisher IJRE Publisher
 
Date 2019-03-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/374
10.21276/ijre.2019.6.2.2
 
Source International Journal of Research and Engineering; Vol 6 No 2 (2019): March 2019 Edition; 580-588
2348-7860
2348-7852
 
Language eng
 
Relation https://digital.ijre.org/index.php/int_j_res_eng/article/view/374/336
 
Rights Copyright (c) 2019 Sai Maung Maung Zaw, Hnin Mya Aye
http://creativecommons.org/licenses/by/4.0
 

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