International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
Automated Cricket Highlight Generation using Advanced Video Processing Techniques Dr. Shraddha Khonde1, Shraddha Lalbegee2, Rida Shaikh3, Afifa Shaikh4 and Saniya Sayyed5 Professor, Dept. of computer Engineering, Modern Education Society’s Wadia College of Engineering Pune, Maharashtra, India Student, Dept. of computer Engineering, Modern Education Society’s Wadia College of Engineering Pune, Maharashtra, India Student, Dept. of computer Engineering, Modern Education Society’s Wadia College of Engineering Pune, Maharashtra, India Student, Dept. of computer Engineering, Modern Education Society’s Wadia College of Engineering Pune, Maharashtra, India Student, Dept. of computer Engineering, Modern Education Society’s Wadia College of Engineering Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This project focuses on automating the
videos that capture the excitement and essence of the game. With matches often extending beyond three hours, the process requires identifying key events such as boundaries, wickets, and game-defining moments to craft a compelling 7–8 minute highlight reel. Traditional methods rely heavily on manual efforts from skilled analysts, making the process time-consuming and prone to inconsistencies.
generation of cricket highlights using a unique integration of advanced computer vision and machine learning techniques, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Optical Character Recognition (OCR), and pose recognition. Cricket, a sport with intricate rules and lengthy matches, often demands labor-intensive efforts to create concise and engaging highlights. Our approach emphasizes innovation by combining event-driven and excitement-based features to identify and trim key moments such as boundaries, wickets, and significant milestones.
This project introduces a novel framework that integrates cutting-edge technologies to automate highlight generation. OCR is employed to analyze and interpret scoreboard data, SVM detects changes in audio intensity and crowd reactions, while ANN leverages pose recognition to identify player and umpire actions. By combining these advanced methodologies, the system ensures a comprehensive approach to event detection, addressing the challenges of accuracy, scalability, and efficiency in highlight creation.
By utilizing crowd reactions, player celebrations, scoreboard changes, and audio intensity variations, the system offers an accurate and dynamic way to detect critical match events. To ensure reliability, the generated highlights are cross-validated with official highlight reels and subjected to manual error-checking, emphasizing precision and completeness. This automation not only saves time and resources but also provides cricket fans with high-quality, accessible content tailored to their busy schedules, thereby revolutionizing the traditional approach to sports video summarization.
Unlike conventional methods, this automated solution dynamically analyzes multiple data streams— visual, auditory, and statistical—simultaneously, ensuring that the most exciting moments are captured without manual intervention. The result is a transformative way to engage cricket fans, offering them immersive and highquality content in a fraction of the time required by traditional processes.
Key Words:
Machine learning, Computer vision, Support Vector Machines, Artificial Neural Networks, Optical Character Recognition, Pose recognition, Event detection, Excitement-based analysis, Video summarization, Sports highlights
1.2 Existing System The conventional cricket highlight production system depends significantly on manual video editing, which is time-consuming, labor-intensive, and susceptible to human error. Human editors normally watch hours of footage, flag important events, and manually cut highlight clips. These operations are human intuition-based and are often subject to subjective judgment.
1.INTRODUCTION 1.1 Cricket Highlight Generation Creating cricket highlights involves the intricate task of transforming hours-long match footage into concise
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