Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks

Augmented reality (AR) has been used in maintenance, simulation, and remote assistance, among other applications. In AR systems, one of the significant issues is the placement of objects in augmented physical environments. Given the importance of object placement in AR systems, we proposed deep learning-based object placement, covering both object detection and object segmentation, to address relevant issues. Deep learning can help users complete tasks by providing the right information effectively, with the method taking into account dynamically changing environments and users’ situations in real time. The problem is that it is rarely used in AR, thereby prompting the combination of deep learning-based object detection and instance segmentation with wearable AR technology to improve the performance of complex tasks. This challenge was addressed in this work through the use of convolutional neural networks in the detection and segmentation of objects in actual environments. We measured the performance of AR technology on the basis of detection accuracy under environmental conditions of different intensities. Experimental results showed satisfactory segmentation and accurate detectio