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Detection of robbery-related concepts using deep learning

Detecting robbery-related concepts or any particular violent scenes in videos is one of the most fundamental on-going work in the world of computer vision. While it is evident that there are more discovery and improvements of such detection task especially in the realm of fully supervised settings,...

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Main Author: Vivaaindrean, Ng Shamir Ng
Format: Final Year Project / Dissertation / Thesis
Published: 2020
Subjects:
Q Science (General)
Online Access:http://eprints.utar.edu.my/3940/1/17ACB00362_FYP.pdf
http://eprints.utar.edu.my/3940/
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spelling my-utar-eprints.39402021-01-07T08:02:29Z Detection of robbery-related concepts using deep learning Vivaaindrean, Ng Shamir Ng Q Science (General) Detecting robbery-related concepts or any particular violent scenes in videos is one of the most fundamental on-going work in the world of computer vision. While it is evident that there are more discovery and improvements of such detection task especially in the realm of fully supervised settings, the acquisition of labelled training data at video’s temporal-level is not sensible. We instead tackle this problem by proposing two novel approaches – MIL-Ranking as well as TAL. At its very core, both aforementioned methods only necessitates ground-truth at video-level, instead of temporallevel. We show that the implementation of MIL and TAL approaches on the huge-scale UCF-Crime dataset demonstrates their capabilities in detecting violent-related concepts at video’s temporal-level. 2020-05-15 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3940/1/17ACB00362_FYP.pdf Vivaaindrean, Ng Shamir Ng (2020) Detection of robbery-related concepts using deep learning. Final Year Project, UTAR. http://eprints.utar.edu.my/3940/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
spellingShingle Q Science (General)
Vivaaindrean, Ng Shamir Ng
Detection of robbery-related concepts using deep learning
description Detecting robbery-related concepts or any particular violent scenes in videos is one of the most fundamental on-going work in the world of computer vision. While it is evident that there are more discovery and improvements of such detection task especially in the realm of fully supervised settings, the acquisition of labelled training data at video’s temporal-level is not sensible. We instead tackle this problem by proposing two novel approaches – MIL-Ranking as well as TAL. At its very core, both aforementioned methods only necessitates ground-truth at video-level, instead of temporallevel. We show that the implementation of MIL and TAL approaches on the huge-scale UCF-Crime dataset demonstrates their capabilities in detecting violent-related concepts at video’s temporal-level.
format Final Year Project / Dissertation / Thesis
author Vivaaindrean, Ng Shamir Ng
author_facet Vivaaindrean, Ng Shamir Ng
author_sort Vivaaindrean, Ng Shamir Ng
title Detection of robbery-related concepts using deep learning
title_short Detection of robbery-related concepts using deep learning
title_full Detection of robbery-related concepts using deep learning
title_fullStr Detection of robbery-related concepts using deep learning
title_full_unstemmed Detection of robbery-related concepts using deep learning
title_sort detection of robbery-related concepts using deep learning
publishDate 2020
url http://eprints.utar.edu.my/3940/1/17ACB00362_FYP.pdf
http://eprints.utar.edu.my/3940/
_version_ 1688551796099776512
score 13.211869

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