Video Summerization

ABSTRACT: –

Video is one of the robust sources of information and the consumption of online and offline videos has reached an unprecedented level in the last few years. A fundamental challenge of extracting information from videos is a viewer has to go through the complete video to understand the context, as opposed to an image where the viewer can extract information from a single frame. In this project, we attempt to employ different Algorithmic methodologies including local features and deep neural networks along with multiple clustering methods to find an effective way of summarizing a video by interesting keyframe extraction.

SYSTEM:-

The video summary system takes video ideas and uses smart tools to extract short, rich video content. The system will have the following modules:

• Video Preprocessing: The system will preprocess the video to extract features such as color, sound and tone. This model uses techniques such as pattern analysis, keyframe extraction and border placement to identify and classify videos.

• Feature extraction: The system can be used for object recognition, face detection, voice recognition, etc. It will extract the relevant features from the previous video, such as This model uses deep learning techniques such as convolutional neural networks (CNN) and neural networks (RNN) to extract high-level features from videos.
• Video content: The system will use feature extraction to generate video content. This model uses a variety of techniques such as clustering, sorting, and optimization to select the most important parts of the video and create a short summary.

• User Interaction: User interaction options can be provided, such as adjusting the length of the content or selecting specific videos to include or exclude.

• Evaluation: The quality of the content created will be evaluated using criteria such as F1 score, precision and recall. This model will use human testers to check the quality of the content and provide feedback to improve the system.

The video summarization system offers a fast and efficient way to create long and complex video content, making it suitable for many applications such as video surveillance, media monitoring and video search. The system will use technology to extract relevant features and create short content, saving time and effort for users who need to review a large number of videos.

PROPOSED SYSTEM:-

“Video summarization is the process of distilling a raw video into a more compact form without losing much information.” Video summarization helps users to navigate through a large sequence of videos and retrieve ones that are most relevant to the query.

Today’s society is fast-paced, constantly endorsing ways of multitasking and efficiency, therefore, it is not all too surprising that this way of life is all about saving time to do more. This gives us the opportunity to build a place where users can use deep learning techniques like Video summarisation as it helps in efficient storage, quick browsing, and retrieval of large collections of video data without losing important aspects.

Time being the most important resource, it’s important to find more ways to enhance productivity. A recommendation and categorisation system along with a summariser would solve the problem of wasting too much time to find the perfect resources. This would help people to be provided with the best content without them being overwhelmed with the huge pool of data available on the internet.

MODULES:-

• Video Pre-Processing Module: This module will pre-process the video to extract related features such as color, tone and sound. It may include techniques such as upsampling, keyframe removal and border placement to define and segment the video. The output of this module will be a set of features extracted from the video that will be used by subsequent modules.

• Feature Extraction Module: This module will extract advanced features such as object recognition, face recognition and speech recognition from pre-recorded videos. It can extract these features using deep learning techniques such as convolutional neural networks (CNN) and recurrent neural networks (RNN).
• The output of this module will be the high-level processing extracted from the video that will be used by the next module.

• Video Summarization Module: This module will generate video summarization using feature extraction. It can use various techniques such as grouping, sorting and optimization to select the most important parts of the video and create a short summary. The output of this module will be the video content that holds the most important part of the video.

• User Interaction Module: This module will provide options for user interaction such as adjusting the length of the content or selecting specific videos to include or exclude.
• It will include a graphical user interface (GUI) that allows the user to interact with the system and edit the content.

• Evaluation Module: This module will evaluate the quality of the created content using indicators such as F1 score, accuracy and recall. It can use human testers to check the quality of the content and provide feedback to improve the system. The output of this module will measure the quality of the generated content.

Together, these models use artificial intelligence to create a complete video summarization system that can complete long and complex explanations.

APPLICATION:-

The app, called VideoSummarize, allows users to upload videos and use artificial intelligence to create a summary of the video’s most important parts. Here are the main features of the app:

• Video Upload: User can upload video from local storage or provide URL of online video.

• Preprocessing: Preprocessing of video to extract color, motion, sound and other related features. This step will include techniques such as upsampling, keyframe removal and border placement to identify and classify video.

• Feature Extraction: Extract advanced features from pre-processed video using deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
• These features can include object recognition, face recognition, and speech recognition.

• Video Summarization: Extracted features are used to create video summaries. Use various techniques such as grouping, sorting and optimization to select the most important parts of the video and create a short summary.

• User Interaction: Users can edit the content by interacting with the system by adjusting the length of the content or selecting specific videos to include or exclude. The system provides a Graphical User Interface (GUI) that allows users to interact with the system.
• Evaluation: Evaluate the quality of created content using metrics such as F1 score, precision, and recall. Human reviewers can also be used to analyze the quality of the content and provide feedback to improve the system.

• Synopsis: Create content and present it to users in a variety of formats, such as short videos, memos, or keyframe sets.

• DOWNLOAD OR SHARE: Users can download the generated content or share it with others via social media, email or other channels.

Individuals, businesses or organizations can use this app to create long and complex video content, saving time and effort compared to watching the entire video manually.

HARDWARE AND SOFTWARE REQUIREMENTS:-

HARDWARE:-

No Hardware Used

SOFTWARE:-

• Upload Bar for uploading URL links.
• Box for Recommended Video.
• Button to submit feedback.
• GO Button to upload the Link.