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 this https URL By sharing
these features with the broader community, we hope to accelerate the research
into soccer video understanding.

本文提出了一种基于多阶段范式的方法，通过使用动作认知模型提取高级语义特征和设计转换器来定位目标事件，以检测和确定足球广播视频中事件的发生时间，并在 SoccerNet-v2 Challenge 中获得了最新的表现，在动作识别和重播地面任务上均表现出最先进的性能。