Abstract
The main work of human motion gesture recognition is to recognize and analyze the behavior of human objects in the video. Although the current research in the field of human motion gesture recognition has achieved certain results, the human motion gesture recognition in real life scenes has great effects due to factors such as camera movement, target scale transformation, dynamic background, viewing angle, and illumination. This article first proposes a new method of constructing human motion posture features to describe human behavior. This method is based on deep convolutional neural network features and topic models. Experiments have verified that compared with the traditional feature map extracted from the convolutional neural network fully connected layer, the feature map extracted from the convolutional neural network convolutional layer is not only lower in dimension but also has higher discrimination. Secondly, based on the feature map of the convolutional neural network, the training map downsampling strategy is used to overcome the interference caused by the object's scale change and shape change. Finally, based on the basketball gesture recognition method, the behavior performance of the legs and arms in 9 basketball actions of walking, running, jumping, standing dribbling, walking dribbling, running dribbling, shooting, passing and receiving is analyzed. As well as the corresponding signal waveform characteristics, a two-stage data division method for basketball is proposed. The unit action data is extracted for analysis to realize feature extraction. In order to select the most suitable classifier for basketball gesture recognition, the constructed feature vector uses four Different classifiers are trained to construct different classifiers to realize the division of actions.
Highlights
Under the current technical background, video human motion gesture recognition is widely used in smart wearable devices, security monitoring, human-computer interaction, sports training and competition, military, medical care and other fields
Z axis., Using k-nearest neighbor algorithm as the classifier, introducing curve similarity as the criterion for distance judgment in the algorithm, closely combining human motion gesture recognition and analyzing the recognition method in all aspects, and determining the optimal k value and polynomial for parameters such as the number of times and the number of data points contained in each group of behaviors, the recognition rate can reach over 96%
In the human motion gesture recognition system using the improved random forest as the classifier, the object of study is the data obtained by extracting the feature vector from the feature signal value of the training image CNN
Summary
Under the current technical background, video human motion gesture recognition is widely used in smart wearable devices, security monitoring, human-computer interaction, sports training and competition, military, medical care and other fields. X. Bu: Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features facial expressions. Based on the VGG-Net model, combined with the traditional optical flow characteristics, the optical flow graph is regarded as an image, and a dual data stream deep convolutional neural network is proposed [16], [17]. Deep learning Some simple non-linear models can be used to transform the original data into more abstract, more complex, higher-level expressions, and more complex functions can be learned through enough model combinations [24], which can be substituted The feature extraction process of video human motion gesture recognition. Proves that the VGG-16 model trained on ImageNet has a good feature description ability on the video human motion gesture recognition data set.
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