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

• URL: http://arxiv.org/abs/2106.14447v1
• Date: Mon, 28 Jun 2021 08:00:21 GMT
• Title: Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection
• Authors: Xin Zhou, Le Kang, Zhiyu Cheng, Bo He, Jingyu Xin
• Abstract summary: 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. 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.
• Score: 3.7709686875144337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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