The team sports game video features complex background, fast target movement, and mutual occlusion between targets, which poses great challenges to multiperson collaborative video analysis. This paper proposes a video semantic extraction method that integrates domain knowledge and in-depth features, which can be applied to the analysis of a multiperson collaborative basketball game video, where the semantic event is modeled as an adversarial relationship between two teams of players. We first designed a scheme that combines a dual-stream network and learnable spatiotemporal feature aggregation, which can be used for end-to-end training of video semantic extraction to bridge the gap between low-level features and high-level semantic events. Then, an algorithm based on the knowledge from different video sources is proposed to extract the action semantics. The algorithm gathers local convolutional features in the entire space-time range, which can be used to track the ball/shooter/hoop to realize automatic semantic extraction of basketball game videos. Experiments show that the scheme proposed in this paper can effectively identify the four categories of short, medium, long, free throw, and scoring events and the semantics of athletes’ actions based on the video footage of the basketball game.

中文翻译：

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结合领域知识和深度特征的篮球比赛视频语义提取

团队运动比赛视频背景复杂，目标移动速度快，目标之间相互遮挡，对多人协同视频分析提出了很大的挑战。本文提出了一种融合领域知识和深度特征的视频语义提取方法，可应用于多人协作篮球比赛视频的分析，其中语义事件被建模为两队球员之间的对抗关系。我们首先设计了一种结合双流网络和可学习时空特征聚合的方案，可用于视频语义提取的端到端训练，以弥合低级特征和高级语义事件之间的差距。然后，提出了一种基于来自不同视频源的知识的算法来提取动作语义。该算法在整个时空范围内收集局部卷积特征，可用于跟踪球/射手/篮筐，实现篮球比赛视频的自动语义提取。实验表明，本文提出的方案可以有效识别短、中、长、罚球和得分四类事件，以及基于篮球比赛视频片段的运动员动作语义。