Precise Action Spotting in untrimmed soccer videos

Abstract

Action detection involves locating the precise moment when each action occurs in a long, untrimmed video. Most prior work on football action spotting has been conducted on broadcast footage; we instead study the task on the GameOn dataset, which provides panoramic camera coverage and synchronised player and ball tracking, but introduces several challenges: actions are densely annotated and temporally close to one another, the panoramic view provides comparatively weak visual evidence, and combining the visual and tracking modalities without destabilising training is a non-trivial design problem. This thesis addresses these challenges on two fronts. First, we conduct a systematic study of temporal modeling architectures for spotting, comparing single-scale recurrent, convolutional, and attention-based modules and proposing a multi-scale Temporal Encoder–Decoder (TED) module that integrates long-range context while preserving frame-level resolution. Second, we study the integration of the tracking modality through a Graph Isomorphism Network and identify intermediate auxiliary supervision over both modality branches as a structural prerequisite for the multimodal model to outperform either modality in isolation. The proposed multimodal architecture reaches 75.27 aMAP@1 and 77.35 aMAP@2 on the held-out test split, improving on the vision-only and tracking-only baselines by +6.42 and +7.22 aMAP@1 respectively. The full implementation is publicly available.