Treffer: Design thinking for deep learning-based metadata video analysis of YouTube video.

The Extraction of data from videos is crucial and likely to be used in many fields for investigation and data collection purposes. Video analysis has been one of the topics for debate for a long time as much data can be extracted from a video. In this article, we have made a prototype of video analyzing software that extracts useful data from the video with the help of various techniques. Our project primarily focuses on the analysis of YouTube videos. To create a user- friendly graphical interface for our tool, we have utilized the Python Tkinter library. We also used the Matplotlib library for data visualization. Deep learning algorithms using TensorFlow, scikit-learn, pandas, CV tool, and imutils libraries for object and action analysis are utilized in our tools for data extraction from the videos. Our tool incorporates sentiment analysis as well, enabling us to analyze the emotional tone of a video and measure its impact on viewers. The data extracted from the video is stored in a database for further processing. This tool can be developed to be utilized in real-time video analysis. Overall, our goal with this project is to provide an automated video analysis for cyber-security by leveraging these powerful algorithms and technologies. [ABSTRACT FROM AUTHOR]

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