Viewport Tracking Model for Automatic Observing in League of Legends

In esports broadcasting, human observers are tasked with providing viewers with a satisfying view of the event. Existing approaches focus primarily on detecting events and often fail to address how the viewing camera should transition after an event is detected. In this study, we defined the viewing process as event detection and viewport tracking, which is the process of following the detected event. In addition, we focus more on viewport tracking and propose a ConvLSTM-based encoder-decoder model based on the viewing data of professional observers. The model aims to automatically predict how observers follow events by learning their temporal patterns and spatial characteristics, and to build a more natural and effective esports automatic broadcasting system. The viewport tracking model was evaluated using Intersection over Union(IoU) and achieved a performance of 0.6689. This result represents a novel attempt to model the viewing sequence after event detection, which offers the potential to enhance the naturalness of automated broadcasting systems.