Automatic parental guide ratings for short movies
Video description is helpful for automatic movie ratings and annotating parental guides. However, human-annotated ratings are somewhat ambiguous depending on the types of movies and demographics. This project proposes a Machine-learning (ML) pipeline to generate a parental rating for short movies au...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2021
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| Online Access: | http://eprints.utar.edu.my/4250/1/17ACB05093_FYP.pdf http://eprints.utar.edu.my/4250/ |
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| _version_ | 1833428572906192896 |
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| author | Chai, Zi Xu |
| author_facet | Chai, Zi Xu |
| author_sort | Chai, Zi Xu |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Video description is helpful for automatic movie ratings and annotating parental guides. However, human-annotated ratings are somewhat ambiguous depending on the types of movies and demographics. This project proposes a Machine-learning (ML) pipeline to generate a parental rating for short movies automatically. The ML pipeline infers and resolves various entities from 5 custom trained ML models trained using a corresponding public dataset. These ML models include nudity scene detection, violent scene detection, profanity scene detection, alcohol & drugs detection. A nudity detection scene is trained using YOLOv4 to detect possible scenes exposing private parts and genitals. Meanwhile, violent scene detection is trained using custom RNN-LSTM to detect possible fighting and gore scenes. Next, the profanity detection uses Google Text-to-Speech API to transcribe audio before feeding it into a custom better-profanity library. Lastly, the alcohol & drug models are trained using features extracted from VGG-16 then fed into a one-class CNN classifier. The experimental result showed that the proposed automatic rating is highly accurate when compared to manually annotated ratings. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.4250 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2021 |
| record_format | eprints |
| spelling | my-utar-eprints.42502022-03-09T13:13:21Z Automatic parental guide ratings for short movies Chai, Zi Xu Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Video description is helpful for automatic movie ratings and annotating parental guides. However, human-annotated ratings are somewhat ambiguous depending on the types of movies and demographics. This project proposes a Machine-learning (ML) pipeline to generate a parental rating for short movies automatically. The ML pipeline infers and resolves various entities from 5 custom trained ML models trained using a corresponding public dataset. These ML models include nudity scene detection, violent scene detection, profanity scene detection, alcohol & drugs detection. A nudity detection scene is trained using YOLOv4 to detect possible scenes exposing private parts and genitals. Meanwhile, violent scene detection is trained using custom RNN-LSTM to detect possible fighting and gore scenes. Next, the profanity detection uses Google Text-to-Speech API to transcribe audio before feeding it into a custom better-profanity library. Lastly, the alcohol & drug models are trained using features extracted from VGG-16 then fed into a one-class CNN classifier. The experimental result showed that the proposed automatic rating is highly accurate when compared to manually annotated ratings. 2021-04-15 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4250/1/17ACB05093_FYP.pdf Chai, Zi Xu (2021) Automatic parental guide ratings for short movies. Final Year Project, UTAR. http://eprints.utar.edu.my/4250/ |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Chai, Zi Xu Automatic parental guide ratings for short movies |
| title | Automatic parental guide ratings for short movies |
| title_full | Automatic parental guide ratings for short movies |
| title_fullStr | Automatic parental guide ratings for short movies |
| title_full_unstemmed | Automatic parental guide ratings for short movies |
| title_short | Automatic parental guide ratings for short movies |
| title_sort | automatic parental guide ratings for short movies |
| topic | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.utar.edu.my/4250/1/17ACB05093_FYP.pdf http://eprints.utar.edu.my/4250/ |
| url_provider | http://eprints.utar.edu.my |