Highlight detection in sports videos has a broad viewership and huge commercial potential. It is thus imperative to detect highlight scenes more suitably for human interest with high temporal accuracy. Since people instinctively suppress blinks during attention-grabbing events and synchronously generate blinks at attention break points in videos, the instantaneous blink rate can be utilized as a highly accurate temporal indicator of human interest. Therefore, in this study, we propose a novel, automatic highlight detection method based on the blink rate. The method trains a one-dimensional convolution network (1D-CNN) to assess blink rates at each video frame from the spatio-temporal pose features of figure skating videos. Experiments show that the method successfully estimates the blink rate in 94% of the video clips and predicts the temporal change in the blink rate around a jump event with high accuracy. Moreover, the method detects not only the representative athletic action, but also the distinctive artistic expression of figure skating performance as key frames. This suggests that the blink-rate-based supervised learning approach enables high-accuracy highlight detection that more closely matches human sensibility.

中文翻译：

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估计花样滑冰视频中亮点检测的眨眼概率

体育视频中的亮点检测具有广泛的收视率和巨大的商业潜力。因此，必须以高时间精度检测更适合人类兴趣的高光场景。由于人们在吸引注意力的事件期间本能地抑制眨眼，并在视频中的注意断点处同步产生眨眼，因此瞬时眨眼率可以用作人类兴趣的高度准确的时间指标。因此，在本研究中，我们提出了一种基于眨眼率的新型自动高光检测方法。该方法训练一维卷积网络 (1D-CNN) 以根据花样滑冰视频的时空姿势特征评估每个视频帧的眨眼率。实验表明，该方法成功估计了 94% 视频剪辑中的眨眼率，并高精度地预测了跳跃事件周围眨眼率的时间变化。此外，该方法不仅检测具有代表性的运动动作，还检测花样滑冰表演的独特艺术表现作为关键帧。这表明基于眨眼率的监督学习方法可以实现更接近人类敏感性的高精度高光检测。