  With rapidly evolving internet technologies and emerging tools, sports
related videos generated online are increasing at an unprecedentedly fast pace.
To automate sports video editing/highlight generation process, a key task is to
precisely recognize and locate the events in the long untrimmed videos. In this
tech report, we present a two-stage paradigm to detect what and when events
happen in soccer broadcast videos. Specifically, we fine-tune multiple action
recognition models on soccer data to extract high-level semantic features, and
design a transformer based temporal detection module to locate the target
events. This approach achieved the state-of-the-art performance in both two
tasks, i.e., action spotting and replay grounding, in the SoccerNet-v2
Challenge, under CVPR 2021 ActivityNet workshop. Our soccer embedding features
are released at https://github.com/baidu-research/vidpress-sports. By sharing
these features with the broader community, we hope to accelerate the research
into soccer video understanding.
