Automated Video Summarizer

Generating dynamic highlight reels from raw video footage

Introduction

Manually creating video highlights from long-form footage is time-consuming and repetitive. This project provides an automated solution using Python to generate concise summaries based on provided timestamp data.

It’s ideal for sports events, lectures, surveillance, or any scenario where you want to focus only on the moments that matter.

Problem

Long videos often contain large chunks of inactive or irrelevant content. Manually scrubbing through hours of footage to extract highlights can take even longer than watching the video itself.

Objective

The goal was to:

• Automate the creation of highlight videos.
• Enable flexible input via CSV with custom timestamps.
• Produce a seamless and high-quality summary video.

Implementation

The summarizer was built using the following stack:

• Python for scripting and automation.
• Pandas to handle the CSV with Start/Stop timestamps.
• MoviePy to extract subclips and stitch them together into one video.

Each row in the CSV is read as a clip interval. The final output is a concatenation of all clips in chronological order.

Outcome

• Reduced highlight creation time from hours to minutes.
• Created compact summaries for match analysis and social media sharing.
• Scalable approach that works with various video lengths and formats.

Conclusion & Next Steps

• Integrate with cloud storage for batch video summarization.
• Explore audio analysis or computer vision to generate timestamps automatically.

This tool streamlines content creation and is a step toward fully automated video editing workflows.