Automatic Video Summarization Using HEVC and CNN Features
A Master of Science thesis in Computer Engineering by Obada Issa entitled, “Automatic Video Summarization Using HEVC and CNN Features”, submitted in November 2022. Thesis advisor is Dr. Tamer Shanableh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Con...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/25177 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513444161519616 |
|---|---|
| author | Issa, Obada |
| author_facet | Issa, Obada |
| author_role | author |
| dc.contributor.none.fl_str_mv | Shanableh, Tamer |
| dc.creator.none.fl_str_mv | Issa, Obada |
| dc.date.none.fl_str_mv | 2022-11 2023-03-02T07:48:03Z 2023-03-02T07:48:03Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2022.58 http://hdl.handle.net/11073/25177 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Static Video Summarization Convolution Neural Networks (CNN) Duplicate Frames Sparse Autoencoders Random Forests classifier Video Coding High Efficiency Video Codec (HEVC) Motion estimation Motion compensation Stepwise regression |
| dc.title.none.fl_str_mv | Automatic Video Summarization Using HEVC and CNN Features |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Computer Engineering by Obada Issa entitled, “Automatic Video Summarization Using HEVC and CNN Features”, submitted in November 2022. Thesis advisor is Dr. Tamer Shanableh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_a05dec13887a15e9dd83508e4c5058a5 |
| identifier_str_mv | 35.232-2022.58 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25177 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Automatic Video Summarization Using HEVC and CNN FeaturesIssa, ObadaStatic Video SummarizationConvolution Neural Networks (CNN)Duplicate FramesSparse AutoencodersRandom Forests classifierVideo CodingHigh Efficiency Video Codec (HEVC)Motion estimationMotion compensationStepwise regressionA Master of Science thesis in Computer Engineering by Obada Issa entitled, “Automatic Video Summarization Using HEVC and CNN Features”, submitted in November 2022. Thesis advisor is Dr. Tamer Shanableh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).With the incredible surge of the internet and surveillance footage, there is a vast number of digital videos. The need to summarize these videos within databases is very crucial. This is where video summarization comes in handy. Video summarization can be achieved by a number of techniques. This study proposes a novel solution for the detection of key-frames for static video summarization. We preprocessed the well-known video datasets by coding them using the HEVC video coding standard. During coding, 64 proposed features were generated from the coder for each frame. Additionally, we extracted RGB frames from the original raw videos and fed them into pre-trained CNN networks for feature extraction. These include GoogleNet, AlexNet, Inception-ResNet-v2, and VGG16. The modified datasets are made publicly available to the research community. A subset of the proposed HEVC feature set was used to identify duplicate or similar frames and eliminate them from the video. We also propose an elimination solution based on the sum of the absolute differences between a frame and its motion-compensated predecessor. The proposed solutions are compared with existing works based on an SIFT flow algorithm that uses CNN features. Subsequently, an optional dimensionality reduction based on stepwise regression was applied to the feature vectors prior to detecting key-frames. The proposed solution is compared with existing studies that use sparse autoencoders with CNN features for dimensionality reduction. The accuracy of the proposed key-frame detection system was assessed using the Positive Predictive Values, Sensitivity, and F-score metrics. Combining the proposed solution with Multi-CNN features and using a Random Forests classifier, it was shown that the proposed solution achieved an average F-score of 0.98.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE)Shanableh, Tamer2023-03-02T07:48:03Z2023-03-02T07:48:03Z2022-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2022.58http://hdl.handle.net/11073/25177en_USoai:repository.aus.edu:11073/251772025-06-26T12:34:34Z |
| spellingShingle | Automatic Video Summarization Using HEVC and CNN Features Issa, Obada Static Video Summarization Convolution Neural Networks (CNN) Duplicate Frames Sparse Autoencoders Random Forests classifier Video Coding High Efficiency Video Codec (HEVC) Motion estimation Motion compensation Stepwise regression |
| status_str | publishedVersion |
| title | Automatic Video Summarization Using HEVC and CNN Features |
| title_full | Automatic Video Summarization Using HEVC and CNN Features |
| title_fullStr | Automatic Video Summarization Using HEVC and CNN Features |
| title_full_unstemmed | Automatic Video Summarization Using HEVC and CNN Features |
| title_short | Automatic Video Summarization Using HEVC and CNN Features |
| title_sort | Automatic Video Summarization Using HEVC and CNN Features |
| topic | Static Video Summarization Convolution Neural Networks (CNN) Duplicate Frames Sparse Autoencoders Random Forests classifier Video Coding High Efficiency Video Codec (HEVC) Motion estimation Motion compensation Stepwise regression |
| url | http://hdl.handle.net/11073/25177 |