Pose recognition in sports scenes based on deep learning skeleton sequence model
Autor: | Li You, Zhaoqimeng Shan, Fengjun Shen, Chen Li-quan, Jiaxuan Chen |
---|---|
Rok vydání: | 2021 |
Předmět: |
Statistics and Probability
0209 industrial biotechnology Sequence model Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Engineering Pattern recognition 02 engineering and technology Skeleton (category theory) 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
Zdroj: | Journal of Intelligent & Fuzzy Systems. :1-10 |
ISSN: | 1875-8967 1064-1246 |
Popis: | Human skeleton extraction is a basic problem in the field of computer vision. With the rapid progress of science and technology, it has become a hot issue in the field of target detection such as pedestrian recognition, behavior monitoring, and pedestrian gesture recognition. In recent years, due to the development of deep neural networks, modeling of human joints in acquired images has made progress in skeleton extraction. However, most models have low modeling accuracy, poor real-time performance, and poor model availability. problem. Aiming at the above-mentioned human target detection problem, this paper uses the deep learning skeleton sequence model gesture recognition method in sports scenes to study, aiming to provide a gesture recognition method with strong noise resistance, good real-time performance and accurate model. This article uses motion video frame images to train the VGG16 network. Using the network to extract skeleton information can strengthen the posture feature expression, and use HOG for feature extraction, and use the Adam algorithm to optimize the network to extract more posture features, thereby improving the posture of the network Recognition accuracy. Then adjust the hyperparameters and network structure of the basic network according to the training results, and obtain the key poses in the sports scene through the final classifier. |
Databáze: | OpenAIRE |
Externí odkaz: |