Introduction

In today’s fast-paced digital world, audiences no longer have the time or patience to watch an entire game. Instead, they crave the excitement of key highlights—the major actions (sixes, boundaries, goals, or wickets) that define the game. Traditionally, creating these highlight reels has been a manual and time-consuming process, requiring hours of editing effort by production teams.

This can be solved by building a solution that uses AI to automatically detect critical in-game actions and generate clips in real time.

The Challenge

Sports events often span several hours, making it impractical to manually identify and extract every highlight-worthy moment. Broadcasters, editors, and OTT platforms face some key challenges:

1. Time-Consuming Editing – Manually reviewing footage and creating clips demands significant time and human effort. Repeated efforts lead to errors as well.
2. Limited Scalability – Handling multiple matches or tournaments simultaneously stretches resources and slows delivery.
3. Instant Fan Demand – Fans expect highlights in near real-time, especially on OTT platforms and social media, leaving little room for delay.

The Solution: Auto-Clip Generation Using AI

The solution aims at leveraging AI to perform below actions for creation of faster, scalable, and more engaging highlights:

1. Detect specific actions in a game (e.g., a boundary like 4s/6s in cricket, jumpball/dunks in basketball, goals/foul in football).
2. Automatically generate short video clips around those actions of specified duration.
3. Allow users to view, export, download, or share these clips seamlessly.

This can be achieved by using an AI model Roboflow.

Role of Roboflow in Auto-Clip Generation

At the heart of the auto-clip generation system lies Roboflow, a powerful computer vision platform that simplifies how AI models are built and deployed. Instead of manually coding complex detection models, Roboflow enables automatic detection or classification from visuals, to create accurate, scalable solutions quickly and efficiently.

By leveraging Roboflow, the system can accurately detect highlight-worthy moments and generate clips in near real time, making it possible to meet the instant content demands of OTT platforms, social media, and fans worldwide.

The model works in below steps:

1. Data Management – Upload full game videos, break them into frames, label the frames (e.g., ball hit, boundary, six, wicket), and organize datasets efficiently. Here the custom object detection models are trained on sports-specific datasets.
2. Preprocessing & Augmentation – Sports events happen under varying conditions—different stadiums, lighting, or camera angles. Roboflow’s preprocessing and augmentation techniques ensure that models remain robust across these variations. It applies transformations (e.g., resizing, rotation, noise) so the model learns to detect actions accurately in different conditions. The video frames are labeled and annotated efficiently.
3. Model Training – The custom models are trained. Roboflow supports popular frameworks like TensorFlow and YOLO.
4. APIs for Deployment – Once trained, the model can be deployed via simple APIs, making it easy to integrate into the application.
5. Scalability – It can handle multiple datasets and models for different sports/events without having to reinvent the wheel.

Below framework is used to accelerate the transition from raw footage to an AI-powered detection pipeline.

Framework for transition from raw footage to an AI-powered detection pipeline

The architecture diagram for AI based auto clip generation is structured into 4 layers:

Architecture diagram for AI-based auto-clip generation system

Use Cases

Benefits 

This solution brings speed, accuracy, and consistency to highlight creation while allowing broadcasters, analysts, and fans to focus on enjoying the game rather than editing it.

1. Faster turnaround – Instant highlight generation without manual effort with high accuracy.
2. Scalability – The model can be used for long games and multiple sports.
3. Content reusability – The generated output (clips) can be shared on OTT apps, social media, and analytics platforms for tracking and recommendation purposes.
4. Player Tracking – Identify and follow specific players across the match.
5. Performance Analytics – Measure player movement, ball trajectory, and patterns for coaching or fan engagement.
6. Fan Personalization – Create custom highlight reels tailored to user preferences (e.g., only goals from a favorite striker, only sixes from a cricket star).

Future Scope and Conclusion 

1. The system can be used to analyze any sports or entertainment content and complex in-game actions, delivering real-time highlights and personalized highlights directly to OTT platforms. 
2. The scope of monetization opportunities can be explored through targeted, clip-based advertisements.
3. The approach can be expanded across multiple sports and to detect more complex actions in any game.
4. The results can be integrated with OTT platforms for real-time highlight generation.
5. This approach can also be used across various fields as entertainment, Education & Training to extract key sections from lectures or workshops.

AI in sports is not just about analytics—it’s transforming fan engagement and content delivery. 

The future is clear: fans will no longer have to wait for highlight reels—they’ll get them as the game unfolds.