花样滑冰视频中闪光检测的眨眼概率估计
Estimating Blink Probability for Highlight Detection in Figure Skating Videos
摘要：
体育视频中的亮点检测具有广泛的受众群体和巨大的商业潜力。因此，必须以高时间精度检测更适合人类兴趣的高光场景。由于人们本能地抑制眨眼期间的注意力攫取事件和视频中的注意力断裂点同步生成眨眼，瞬时眨眼率可以被用作人类兴趣的高度准确的时间指标。因此，在这项研究中，我们提出了一种新的，自动高光检测方法的基础上眨眼率。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来评估每个视频帧的眨眼率。实验表明，该方法成功地估计了94%的视频片段中的眨眼率，并预测了跳跃事件周围眨眼率的时间变化，具有较高的准确性。该方法不仅能检测出具有代表性的运动动作，还能检测出花样滑冰表演的独特艺术表现力作为关键帧。这表明基于闪烁率的监督学习方法能够实现更紧密地匹配人类敏感性的高精度高光检测。
英文摘要：
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.
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