Video Summarization Using Deep Learning

This project presents a Video Summarization Tool that processes and summarizes videos by identifying and emphasizing objects of interest. Implemented with Python, OpenCV, and Tkinter, the tool has a simple graphical user interface for video processing. The main functionality is based on the YOLOv3-tiny object detection model, which is optimized for quicker processing. The model, which is trained using OpenCV's DNN module, finds objects in video frames and outlines them with rectangles. The application analyzes videos by bypassing frames and resampling them, storing processed frames in an output video file. It reads and writes in several video formats and outputs the MP4 codec. The most prominent feature is the GUI based on Tkinter, enabling users to load input videos and see the output summary. The interface provides video selection and output buttons for ease of use by users with little technical knowledge.