摘要
Abstract
A Bi-LSTM model based on OpenPose was proposed for human behavior recognition when the input object was a video.Common deep learning algorithms cannot effectively solve the problems of data timing and continuity.Firstly,the experimental dataset was selected,and then OpenPose was used to process the dataset,extracting 18 skeletal joint points of the human body.The data was annotated and labeled accordingly.Bi-LSTM was then used to classify human behavior.To verify its effectiveness,KNN and SVM are compared and analyzed.The experimental results showed that the model could not only improve the recognition performance of the system,but also effectively solved the problem of video timing.The algorithm proposed in this paper has a high recognition accuracy,reaching 0.96,and had a wider range of applications.关键词
深度学习/长短时记忆网络/OpenPose/人体行为识别Key words
deep learning/long short-term memory networks/OpenPose/human action recognition分类
信息技术与安全科学
