论文标题
在运动中进行累积的比赛时间回归：超距离跑步活动中的I3D Convnet转移学习
Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events
论文作者
论文摘要
根据短录像预测运动员的表现是高度挑战的。绩效预测需要高领域知识和足够的证据来推断适当的质量评估。体育专家通常可以实时推断这种信息。在本文中，我们建议对超距离跑步者的累积比赛时间（CRT）进行回归，即自比赛开始以来，跑步者一直在采取行动，仅将镜头作为输入。我们稍微修改了I3D Convnet主链，并为此训练了新添加的回归器。我们使用视觉输入的适当预处理，以从特定的跑步者中进行转移学习。我们表明，由此产生的神经网络可以为短输入素材提供出色的性能：在估计8到20个小时的跑步者的CRT时，平均绝对误差为18分钟半。我们的方法具有几个有利的特性：它不需要人类专家提供任何见解，可以通过观察跑步者在比赛中的任何时刻使用它，并且可以在任何给定时间告知比赛人员有关跑步者的信息。
Predicting an athlete's performance based on short footage is highly challenging. Performance prediction requires high domain knowledge and enough evidence to infer an appropriate quality assessment. Sports pundits can often infer this kind of information in real-time. In this paper, we propose regressing an ultra-distance runner cumulative race time (CRT), i.e., the time the runner has been in action since the race start, by using only a few seconds of footage as input. We modified the I3D ConvNet backbone slightly and trained a newly added regressor for that purpose. We use appropriate pre-processing of the visual input to enable transfer learning from a specific runner. We show that the resulting neural network can provide a remarkable performance for short input footage: 18 minutes and a half mean absolute error in estimating the CRT for runners who have been in action from 8 to 20 hours. Our methodology has several favorable properties: it does not require a human expert to provide any insight, it can be used at any moment during the race by just observing a runner, and it can inform the race staff about a runner at any given time.
