WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that the deep convolutional neural network(DCNN) was used as the core algorithm for image recognition, and an augmented reality system that can recognize and track objects in dynamic scenes in real-time was designed so as to realize the recognition and localization of objects in augmented reality scenes. The DCNN has strong feature extraction and classification ability, which can extract useful feature information from complex images and use it for object recognition and tracking, and large-scale dynamic image datasets are used to train the DCNN in order to improve the recognition accuracy of the network.
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DCNN is a special neural network structure mainly used for image recognition and computer vision tasks. It is composed of multiple convolutional, pooling and fully connected layers, each with a certain number of neurons. The core of DCNN is to achieve image classification and recognition by learning image features. The convolutional layer of a DCNN is its most important component, which extracts the features of an image by using a convolutional kernel that performs convolutional operations on the input image. The convolution kernel is used to obtain the output feature map by sliding over the input image and multiplying it element by element with the image and then summing the results. By stacking multiple convolutional layers, the DCNN can learn different levels of features, from low level to high level, and gradually extract more abstract features. The pooling layer is designed to reduce the size of the feature map and the number of parameters while retaining the most important feature information. Commonly used pooling operations are maximum pooling and average pooling, which take the maximum value or average value of local regions in the feature map as output, respectively. Through the pooling layer operations, the size of the feature map can be reduced, and the translation invariance and noise immunity of the features can be improved. The fully connected layer is the last layer of the DCNN, which spreads the outputs of the convolutional and pooling layers into one-dimensional vectors and classifies them by the neurons in the fully connected layer. Each neuron of the fully connected layer is connected to all the neurons of the previous layer. The fully connected layer learns weights and biases to achieve linear combinations and nonlinear transformations of the input features to obtain the final classification result.
WiMi took DCNN as the base model for image recognition. By training on a large amount of well-labeled image data, the network is allowed to learn the feature representations of different objects and accurately locate and recognize these objects in the input image. In order to accommodate the processing of dynamic images, WiMi adapted the network appropriately for information transfer and tracking between successive frames. Then, the recognized objects are combined with augmented reality to achieve real-time augmented reality effects. By integrating virtual objects with real scenes, it provides users with richer information and interaction.
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This DCNN-based augmented reality dynamic image recognition has great potential for applications in fields such as gaming, education, and healthcare, bringing users a more immersive augmented reality experience. For example, in game development, the technology can be used to realize the recognition of dynamic characters and objects in the game; in intelligent transportation systems, the technology can be used to identify vehicles and pedestrians in the traffic scene; in the industrial field, the technology can be used to identify the equipment and products on the production line and so on. By combining deep learning and augmented reality technology, DCNN-based augmented reality dynamic image recognition technology provides a more accurate and efficient dynamic image recognition method.
DCNN-based augmented reality dynamic image recognition technology has great potential for development in the future. In the future, WiMi will further improve its performance and application scope through research on model optimization, dataset expansion, and multi-modal integration, to provide better support for the applications in the field of augmented reality.
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