Human action recognition is an important part for computers to understand the behavior of people in pictures or videos. In a single image, there is no context information for recognition, so its accuracy still needs to be greatly improved. In this paper, a single-image human action recognition method based on improved ResNet and skeletal keypoints is proposed, and the accuracy is improved by several methods. We improved the backbone network ResNet-50 and CPN to a certain extent and constructed a multitask network to suit the human action recognition task, which not only improves the accuracy but also balances the total number of parameters and solves the problem of large network and slow operation. In this paper, the improvement methods of ResNet-50, CPN, and whole network are tested, respectively. The results show that the single-image human action recognition based on improved ResNet and skeletal keypoints can accurately identify human action in the case of different human movements, different background light, and occlusion. Compared with the original network and the main human action recognition algorithms, the accuracy of our method has its certain advantages.

中文翻译：

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基于改进的ResNet和骨架关键点的单幅人体动作识别算法

人体动作识别是计算机了解图片或视频中人物行为的重要组成部分。在单个图像中，没有用于识别的上下文信息，因此仍需要大大提高其准确性。提出了一种基于改进的ResNet和骨骼关键点的单图像人体动作识别方法，并通过几种方法提高了识别精度。我们对骨干网ResNet-50和CPN进行了一定程度的改进，构建了适合人类动作识别任务的多任务网络，不仅提高了准确性，而且平衡了参数总数，解决了网络规模大，网络速度慢的问题。操作。本文分别测试了ResNet-50，CPN和整个网络的改进方法。结果表明，基于改进的ResNet和骨骼关键点的单图像人体动作识别可以在不同的人体动作，不同的背景光和遮挡情况下准确识别人体动作。与原始网络和主要的人类动作识别算法相比，该方法的准确性具有一定的优势。