https://par.nsf.gov/servlets/purl/10432261
Despite recent advances in deep learning, object detection and tracking still require considerable manual and
computational effort. First, we need to collect and create a database of hundreds or thousands of images of the target
objects. Next we must annotate or curate the images to indicate the presence and position of the target objects within those
images. Finally, we must train a CNN (convolution neural network) model to detect and locate the target objects in new
images. This training is usually computationally intensive, consists of thousands of epochs, and can take tens of hours for
each target object. Even after the model training in completed, there is still a chance of failure if the real-time tracking and
object detection phases lack sufficient accuracy, precision, and/or speed for many important applications. Here we present
a system and approach which minimizes the computational expense of the various steps in the training and real-time
tracking process outlined above of for applications in the development of mixed-reality science laboratory experiences by
using non-intrusive object-encoding 2D QR codes that are mounted directly onto the surfaces of the lab tools to be tracked.
This system can start detecting and tracking it immediately and eliminates the laborious process of acquiring and annotating
a new training dataset for every new lab tool to be tracked.