基于视觉注意力的人体行为识别

视频中人体行为识别是近年来计算机视觉中的一个重要研究领域，但是现有的方法对于视频表示方式存在不足，无法聚焦于图像内的显著区域.提出了一种基于视觉注意力的深度卷积神经网络，可以有效地为视频表示特征附加一个权重，对特征中的有益区域进行注意，实现更加准确的行为识别.在自建的Oilfield-7油田数据集和HMDB51数据集上进行了实验，以此来验证适用于油田现场人体行为所提出的网络模型的有效性.实验结果表明，所提的方法与已取得优异表现的双流架构相比具有一定的优越性.

Human Action Recognition Based on Visual Attention

Recognition of human actions in videos is an important research field in computer vision in recent years. However, existing methods have insufficient representation of video and cannot focus on significant areas within the image. We propose a deep convolutional neural network based on visual attention, which can effectively add a weight to the video representation features, pay attention to the beneficial regions in the features, and achieve more accurate behavior recognition. We conducted experiments on HMDB51 and our own Oilfield-7 dataset to verify the validity of the model proposed for human actions on the oilfield. The experimental results show that the proposed method has certain advantages compared with the two-stream architectures which have achieved excellent performance.