Abstract
For the slow speed and low accuracy of slow motor action recognition methods, this study proposes a motor action analysis method based on the CNN network and the softmax classification model. First, in order to obtain motor action feature information, by using static spatial features of BN-inception based on CNN network extracted actions and high-dimensional features of 3D ConvNet, then based on softmax classifier structure and realizing taxonomic recognition of the motor actions. Finally, through the decision-layer fusion and time semantic continuity optimization strategy, the motion action recognition accuracy is further improved and the more efficient motion action classification recognition is realized. The results show that the proposed method can complete the motor action analysis and achieve the classification recognition accuracy to 83.11%, which has certain practical value.
Highlights
Despite the great progress in motor motion analysis, its overall performance still needs to be improved, mainly due to the blurred boundary of motor motion, which increases the difficulty of the study
To further improve the detection performance of the method according to temporal semantic continuity, this study proposes an optimization strategy based on the characteristics of motion action
With the same classification recognition accuracy, the proposed softmax network in this study has a shorter training time and looks about 10 times shorter than the SVM classifier. is shows that the softmax classification network proposed in this study performs better and is more conducive to motion action analysis
Summary
Movement action analysis is an important branch of computer vision, which involves data mining, image processing, and other content, and is widely used in sports, music playing, and many other scenes. Motion action analysis mainly focuses on motion detection and recognition and has achieved remarkable research results. E results show that the action recognition results of a convolutional neural network with the dual-attention mechanism are comparable to the recognition results of the latest algorithm [2]. E results show that this method has good precision and has great advantages compared with the most advanced algorithms. It can be seen from the above studies that convolutional neural networks are widely used in action recognition, among which the CNN attracts more attention due to its unique characteristics. This study applies powerful deep learning capabilities, based on the CNN network and the softmax classifier, and proposes a deep learning-based motion action analysis method
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