Team Members:

1- Nour Fouad

2- Mohammad Hesham

3- Bishoy Hany

Supervisors:

1- Dr. Eslam Amer

2- Eng. Menna Gamil

Project Description

With the enormous growth rate in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. Video summarization is considered a promising approach for efficacious realization of video content through Identifying and picking out descriptive frames of the video. In this paper, we propose an adaptive framework called Smart-Trailer (S-Trailer) to automate the process of creating an online trailer for any movie based only on its subtitle. The language used in the subtitle is English.The framework analyzes the movie subtitle file to extract relevant textual features that are used to classify the movie into its corresponding genre(s). Experimentation on a real movies data- set showed high accuracy rate (0.89) in classifying movies into their corresponding genre(s). Currently, we employ deep learning techniques to captures user behaviors and opinions in order to adapt our system to provide users with relevant video scenes recommendations that match their preferences.

Proposal:

Proposal Document: STrailer_Proposal

Proposal Presentation: STrailer_Proposal

Software Requirement Specification:

SRS Document: STrailer_SRS

SRS Presentation: STrailer_SRSPPT

SRS Demo: https://youtu.be/ns5izkgnGXY

Software Design Document:

SDD Document: STrailer_SDD

SDD Presentation: STrailer_SDDPPT

SDD Demo: https://youtu.be/KzvyR8ZCElU

Final Thesis:

Thesis Document: STrailer_FinalThesis

Thesis Presentation: STrailer_FinalThesis

Thesis Demo:

Publications:

IEEE Paper: https://ieeexplore.ieee.org/document/8358211/

Graduation Projects Workshop:

Presentation: STrailer_GPWPPT

Flyer:

Roll-up: