Robust tracking of industrial objects across environments from small samples in single environments using chroma-key and occlusion augmentations

Abstract

Training deep models that can be deployed on embedded systems to robustly detect and track highly specialized industrial objects in a variety of field environments remains very challenging. Large Deep Foundation models (e.g., [yuan21]) make it easier than ever to detect and track everyday objects but do not work as well for specialized industrial objects. These models are often very large and not suitable for deployment on embedded systems. In this work we show that the use of a chroma-key like substitution combined with artificial occlusion generation allows one to capture a small number of images of objects under a fixed background in the lab and then generalize them to novel backgrounds that work in the real world under realistic conditions improving detection of occluded objects by 4% and improving detection in different environments by 44% over state-of-the-art augmentation methods such as MOSAIC.