Sports highlight selection has traditionally required expert opinions and manual labor of video editors. To automate this laborious task, crowdsourcing viewers’ live comments has recently emerged as a promising tool, which can remove the burden of extracting semantic information by computer vision. However, popular crowdsourcing methods based on peak-finding are sensitive to noise and may produce deviant highlights from the expert choice. To increase the accuracy of automated selection of sports highlight, we introduce a statistically sound crowdsourcing method, SportLight. In this work, we take a statistical approach that combines multiple hypothesis testing and \(\ell _1\)-trend filtering (fused lasso), supported by a computationally inexpensive algorithm. By analyzing 29 baseball games played in the 2016 and 2017 seasons, we demonstrate that our approach properly reduces the risk of false alarm and generates the results closer to expert-chosen highlights than that of the peak-finding method.

体育亮点选择传统上需要专家意见和视频编辑的手工劳动。为了自动化这项艰巨的任务，众包观众的实时评论最近成为一种很有前途的工具，它可以消除通过计算机视觉提取语义信息的负担。然而，流行的基于寻峰的众包方法对噪声很敏感，可能会从专家的选择中产生异常的亮点。为了提高自动选择体育亮点的准确性，我们引入了一种统计上合理的众包方法 SportLight。在这项工作中，我们采用了一种结合多个假设检验和\(\ell _1\)-趋势过滤（融合套索），由计算成本低廉的算法支持。通过分析 2016 和 2017 赛季的 29 场棒球比赛，我们证明我们的方法适当地降低了误报的风险，并且生成的结果比寻峰方法更接近专家选择的亮点。