Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Asadzadehkaljahi, M; Halder, A; Pal, U; Shivakumara, P

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Asadzadehkaljahi, M Halder, A

Pal, U Shivakumara, P

Permalink

Publisher:: BON VIEW PUBLISHING PTE
Publication Type:: Journal Article
Citation:: Artificial Intelligence and Applications

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (689.89 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Asadzadehkaljahi, M
dc.contributor.author	Halder, A https://orcid.org/0000-0002-8834-8022
dc.contributor.author	Pal, U
dc.contributor.author	Shivakumara, P
dc.date.accessioned	2024-03-15T01:41:35Z
dc.date.available	2024-03-15T01:41:35Z
dc.identifier.citation	Artificial Intelligence and Applications
dc.identifier.issn	2374-4979
dc.identifier.issn	2811-0854
dc.identifier.uri	http://hdl.handle.net/10453/176751
dc.description.abstract	<jats:p>Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatio-temporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera are inconsistent and unstable while for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of frames with static objects and arbitrary movement objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.</jats:p>
dc.language	en
dc.publisher	BON VIEW PUBLISHING PTE
dc.relation.ispartof	Artificial Intelligence and Applications
dc.relation.isbasedon	10.47852/bonviewaia320526
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-15T01:41:34Z
pubs.publication-status	Published online

Abstract:

Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatio-temporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera are inconsistent and unstable while for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of frames with static objects and arbitrary movement objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176751