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Augmented reality (AugmentedReality,AR) is a technology that combines virtual information with the real world, which provides users with a richer and more interactive experience by superimposing virtual elements in real scenes. With the development of computer vision, image recognition has gradually become an important research direction of AR technology.

The current augmented reality system has achieved some results in image recognition in static scenes, but there are still some challenges in dynamic scenes. For example, when the object is moving or the scene changes, the traditional image recognition algorithms are often unable to accurately identify the object or track the position and posture of the object. For this reason, micro-beauty holography (NASDAQ:WIMI) uses deep convolution neural network as the core algorithm of image recognition, and designs an augmented reality system which can identify and track objects in dynamic scenes in real time, so as to realize the recognition and location of objects in augmented reality scenes. Deep convolution neural network has strong feature extraction and classification ability, which can extract useful feature information from complex images and apply it to object recognition and tracking. a large-scale dynamic image data set is used to train the depth convolution neural network to improve the generalization ability and recognition accuracy of the network.

Deep convolution neural network (DeepConvolutional NeuralNetwork, referred to as DCNN) is a special neural network structure, which is mainly used in image recognition and computer vision tasks. It is composed of a number of convolution layers, pooled layers and fully connected layers, and each layer has a certain number of neurons. The core idea of DCNN is to realize image classification and recognition by learning image features. The convolution layer of DCNN is the most important part of it. It uses convolution check to perform convolution operation on the input image to extract the features of the image. The convolution kernel is a set of weights, which is obtained by sliding on the input image and multiplying the image element by element, and then adding the results to get the output feature graph. Through the stacking of multiple convolution layers, DCNN can learn different levels of features, from low-level to high-level, and gradually extract more abstract features. The purpose of pooling layer is to reduce the size and number of parameters of the feature graph, while retaining the most important feature information. The commonly used pooling operations are maximum pooling and average pooling, which take the maximum or average value of the local area in the characteristic graph as the output respectively. Through the operation of the pooling layer, the size of the feature graph can be reduced and the translation invariance and anti-noise of the feature can be improved. The fully connected layer is the last layer of the DCNN, which flattens the output of the convolution layer and the pooled layer into one-dimensional vectors, and classifies or regresses through the neurons of the fully connected layer. The connections between the neurons in the fully connected layer are fully connected, and each neuron is connected to all the neurons in the previous layer. The fully connected layer realizes the linear combination and nonlinear transformation of the input features by learning weights and biases, so as to get the final classification results.

Specifically, WIMI micro-beauty holography first uses the depth convolution neural network as the basic model of image recognition. Through the training of a large number of labeled image data, the network can learn the feature representation of different objects, and accurately locate and identify these objects in the input image. In order to adapt to the processing of dynamic images, WIMI micro-beauty holography adjusts the network appropriately in order to transmit and track information between successive frames. Then, the identified object is combined with augmented reality technology to achieve real-time augmented reality effect. Through the integration of virtual objects and real scenes, it provides users with more abundant information and interactive ways.

The dynamic image recognition technology of augmented reality based on deep convolution neural network studied by WIMI micro-beauty holography has a wide application potential, and can be used in games, education, and other fields to bring users a more immersive augmented reality experience. For example, in game development, this technology can be used to identify dynamic roles and objects in the game; in intelligent transportation systems, this technology can be used to identify vehicles and pedestrians in traffic scenes; in the industrial field, this technology can be used to identify equipment and products on the production line and so on. Through the combination of deep learning and augmented reality technology, augmented reality dynamic image recognition technology based on deep convolution neural network provides a more accurate and efficient dynamic image recognition method.

The dynamic image recognition technology of augmented reality based on deep convolution neural network has great potential in the future. In the future, WIMI micro-beauty holography will further improve the performance and application range of this technology through the research of model optimization, data set expansion, real-time and multi-modal fusion, and provide better support for the application of augmented reality.

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