In the video surveillance of medical institutions, pain intensity is a significant clue to the state of patients. Of late, some approaches leverage various spatio-temporal methods to capture the dynamic pain information of videos for accomplishing pain estimation automatically. However, there is still a challenge in the spatio-temporal saliency, which means pain is always reflected in some important regions of informative image frames in a video sequence. To this end, we propose a deep spatio-temporal attention model called as Pain-Attentive Network (PAN), which pays more attention on the saliency in the extraction of dynamic features. PAN consists of two subnetworks: spatial and temporal subnetwork. Especially, in spatial subnetwork, a proposed spatial attention module is embedded to make the spatial feature extraction more targeted. Also, a devised temporal attention module is inserted in temporal subnetwork, so that the temporal features focus on informative image frames. Extensive experiment results on the UNBC-McMaster Shoulder Pain database show that our proposed PAN achieves compelling performances. In addition, to evaluate the generalization, we report competitive results of our proposed method in the Remote Collaborative and Affective database.

在医疗机构的视频监视中，疼痛强度是了解患者状况的重要线索。最近，一些方法利用各种时空方法来捕获视频的动态疼痛信息，以自动完成疼痛估计。但是，时空显着性仍然存在挑战，这意味着疼痛始终反映在视频序列中信息图像帧的一些重要区域中。为此，我们提出了一种深时空注意模型，称为“疼痛注意网络”（PAN），该模型更加关注动态特征的提取中的显着性。PAN包含两个子网：空间和时间子网。特别是在空间子网中，嵌入了一种提出的空间注意模块，以使空间特征提取更具针对性。也，一个设计好的时间注意模块被插入到时间子网中，使得时间特征集中在信息图像帧上。在UNBC-McMaster肩痛数据库上进行的大量实验结果表明，我们提出的PAN具有令人信服的性能。另外，为了评估一般性，我们在“远程协作和情感”数据库中报告了我们提出的方法的竞争结果。