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With rapidly evolving internet technologies and emerging tools, sportsrelated videos generated online are increasing at an unprecedentedly fast pace.To automate sports video editing/highlight generation process, a key task is toprecisely recognize and locate the events in the long untrimmed videos. In thistech report, we present a two-stage paradigm to detect what and when eventshappen in soccer broadcast videos. Specifically, we fine-tune multiple actionrecognition models on soccer data to extract high-level semantic features, anddesign a transformer based temporal detection module to locate the targetevents. This approach achieved the state-of-the-art performance in both twotasks, i.e., action spotting and replay grounding, in the SoccerNet-v2Challenge, under CVPR 2021 ActivityNet workshop. Our soccer embedding featuresare released at https://github.com/baidu-research/vidpress-sports. By sharingthese features with the broader community, we hope to accelerate the researchinto soccer video understanding.

Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection