Team 28

The main purpose of this SRS document is to illustrate and outline the requirements for our graduation project (Video Bite). That is mainly generating summarized descriptions for videos based on their audio, video, and emotions all together. Besides creating timestamps for the video to describe what is happening in each part. Our aim is to save users who search for video time and make it easier for content creators to generate their own description.

Our system Video bite is aimed to help people dealing with videos. It helps them to find accurate description for any video so they do not waste time watching unrelated videos. The users will get description and timestamps for the selected video. These will also help content creators to generate their video descriptions, keywords and timestamps.

The proposed system implements a new way for summarizing videos based on their audio, video,and emotions all together. That aims to achieve a good readable and accurate description of video. Moreover, the fusion of the summarized texts outputted from audio, video, and emotions. To get one summarized text at the end that could be a meaningful description for the video We conducted a survey to ask about the importance of videos for people. Also to check if they create videos or not and ask about their uploading process. Survey results showed that 73.5% of the respondents did not create or upload videos to any video platforms. However, for the 26.5% who do upload videos, 33.3% of them do not write a description for their videos . Sometimes, they write only a few words that do not describe the actual video content. 87.2% of them agreed if they had such a tool, they would use it.The proposed system overview is shown in figure 1 which includes the following steps:

1. Every couple of seconds, the video is separated into smaller videos. This splitting is done according to scenes hence each video into frames and audio.

2. Each frame of the smaller videos frames is passed through the preprocessing phase. The frames and audio will be preprocessed by removing blurry scenes and noisy audio.

3. The audio is transcribed into text by applying speech to text recognition. The output is a text and then passed to LSTM and CNN models.

4. The three text outputted descriptions are fused together to output a summarized fused meanngful text.

5. Repetition of this process occur to complete the remaining of all smaller videos.

6. An abstractive summarization would be then applied to text using Natural Language Processing (NLP) techniques.

7. NLP is used to produce the video description and timestamps. Having cuts at the scenes and labeling it help finding it later.