论文标题

篮球跟踪数据中的小组活动识别 - 团队运动中的神经嵌入(NETS)

Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)

论文作者

Hauri, Sandro, Vucetic, Slobodan

论文摘要

像许多团队运动一样,篮球涉及两组球员,他们从事合作和对抗性活动以赢得比赛。球员和团队正在执行各种复杂的策略,以比对手获得优势。定义,识别和分析不同类型的活动是体育分析中的一项重要任务,因为它可以导致球员和教练人员更好地策略和决策。本文的目的是自动识别篮球小组的活动,从跟踪代表玩家和球的位置的数据。我们在团队运动中提出了一种新颖的深度学习方法,以实现小组活动识别(GAR)。为了有效地对团队运动中的玩家关系进行建模,我们将基于变压器的体系结构与LSTM嵌入结合在一起,以及一个团队合并层以识别小组活动。培训这样的神经网络通常需要大量注释的数据,这会产生高标签成本。为了解决手动标签的稀缺性,我们在自我监督的轨迹预测任务上产生弱标签并预处理神经网络。我们使用了从632个NBA游戏中的大型跟踪数据集来评估我们的方法。结果表明,NET能够以高准确的方式学习小组活动,并且网络中的自我监督训练对GAR的准确性产生了积极的影响。

Like many team sports, basketball involves two groups of players who engage in collaborative and adversarial activities to win a game. Players and teams are executing various complex strategies to gain an advantage over their opponents. Defining, identifying, and analyzing different types of activities is an important task in sports analytics, as it can lead to better strategies and decisions by the players and coaching staff. The objective of this paper is to automatically recognize basketball group activities from tracking data representing locations of players and the ball during a game. We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS. To efficiently model the player relations in team sports, we combined a Transformer-based architecture with LSTM embedding, and a team-wise pooling layer to recognize the group activity. Training such a neural network generally requires a large amount of annotated data, which incurs high labeling cost. To address scarcity of manual labels, we generate weak-labels and pretrain the neural network on a self-supervised trajectory prediction task. We used a large tracking data set from 632 NBA games to evaluate our approach. The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy.

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