Applying Deep Learning in Augmented Reality Tracking

An existing deep learning architecture has been adapted to solve the detection problem in camera-based tracking for augmented reality (AR). A known target, in this case a planar object, is rendered under various viewing conditions including varying orientation, scale, illumination and sensor noise. The resulting corpus is used to train a convolutional neural network to match given patches in an incoming image. The results show comparable or better performance compared to state of art methods. Timing performance of the detector needs improvement but when considered in conjunction with the robust pose estimation process promising results are shown. © 2016 IEEE

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

SdcNet: A Computation-Efficient CNN for Object Recognition

In many computer-vision systems, object recognition is one of the most commonly-used operations. The challenging task in this operation is to extract sufficient critical features related to the targets from diverse backgrounds. Convolutional neural networks (CNNs) can be used to meet this challenge, which, however, often requires a large amount of computation resources. In this thesis, a computation-efficient CNN architecture for object recognition is proposed. It aims at using the lowest computation volume to achieve a good processing quality. This is achieved by applying image filtering knowledge in the design of the CNN architecture. This work is composed of two parts, the design of a CNN module for feature extraction, and an end-to-end CNN architecture. In the module, in order to extract the maximum amount of high-density feature information from a given set of 2-D maps, successive depthwise convolutions are applied to the same group of data to produce feature elements of various filtering orders. Moreover, a particular pre-and-post-convolution data control method is used to optimize the successive convolutions. The pre-convolution data control is to organize the data to be convolved according to their nature. The post-convolution data control is to combine the critical feature elements of various filtering orders to enhance the quality of the convolved results. The CNN architecture is mainly composed of the cascaded modules. The hyper-parameters in the architecture can be adjusted easily so that each module is tuned to suit the signals in order to optimize the processing quality. The simulation results demonstrated that the architecture gives a better processing quality using a significantly lower computation volume, compared with existing CNNs of the similar kind. The results also confirm the computation efficiency of the proposed module, which enables more object recognition applications on embedded devices