Video highlight generator utilizing a convolutional neural network (cnn) combined with a long short-term memory (lstm) network for mobile legends: bang bang/
Arabella Mae M. Aduviso, Luis Pocholo Caducio, Juliana Anne M. Capoquian, Simone Arabella B. Caturla, and Jieson R. Orain.--
- Manila: Technological University of the Philippines, 2025.
- x, 124pages: 29cm.
Bachelor's thesis
College of Science.--
Includes bibliographic references and index.
To respond to the increase in demand for efficient content creation in Esports, this study presents a Video Highlight Generator for Mobile Legends: Bang Bang using two deep learning architectures. The system merges a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) specifically a Long Short-Term Memory Network (LSTM) into a modified Long-term Recurrent Convolutional Network (LRCN) to detect temporal gameplay patterns such as team fights, and YOLOv8 for real-time in-game banner detection with an 80% confidence threshold. Detected banners are assigned excitement scores to prioritize highlight-worthy moments, considering only events lasting at least three seconds to ensure relevance. The system compiles these into a reviewable highlight reel, allowing manual review and arrangement based on priority or chronology. Developed and trained in Google Colaboratory (Colab) and deployed in a Python-based frontend, the system was evaluated across multiple hardware setups and demonstrated high acceptability in the product quality aspect of the ISO 25010. The study validated the effectiveness of the hybrid deep learning model’s integration in generating automated and customizable gameplay highlights, providing a significant tool for Esports players, teams, and content creators.
Video highlight generation Deep learning architectures Convolutional neural networks (cnn)