With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video.

中文翻译：

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基于 PSO 的 LSTM 神经网络的舞蹈视频中的人体骨骼检测和提取

随着社会信息化水平显着提升，以机器视觉为代表的新兴信息技术被应用到越来越多的场景。其中，基于该技术的舞蹈视频中人体骨骼的检测与提取在教育培训方面有着巨大的市场需求。然而，现有的检测提取技术存在识别速度慢、提取精度低的问题。因此，本文提出一种基于粒子群优化的神经网络来检测和提取舞蹈视频中的人体骨骼。通过在不同数据集上的研究和测试，发现基于粒子群优化算法的神经网络具有良好的检测和提取能力，对于人体骨骼点的检测和识别具有较高的准确率。其中，在所有MPII数据集上，本文提出的PSO-LSTM平均准确率比其他最优算法高3.9%；在PoseTrack数据集上，检测和提取的平均准确率提高了2.3%。以上结果表明，基于粒子群优化的神经网络检测速度快、提取精度好，可用于舞蹈视频中人体骨骼的检测与提取。