  Occlusion is one of the most significant challenges encountered by object
detectors and trackers. While both object detection and tracking has received a
lot of attention in the past, most existing methods in this domain do not
target detecting or tracking objects when they are occluded. However, being
able to detect or track an object of interest through occlusion has been a long
standing challenge for different autonomous tasks. Traditional methods that
employ visual object trackers with explicit occlusion modeling experience drift
and make several fundamental assumptions about the data. We propose to address
this with a `tracking-by-detection` approach that builds upon the success of
region based video object detectors. Our video level object detector uses a
novel recurrent computational unit at its core that enables long term
propagation of object features even under occlusion. Finally, we compare our
approach with existing state-of-the-art video object detectors and show that
our approach achieves superior results on a dataset of furniture assembly
videos collected from the internet, where small objects like screws, nuts, and
bolts often get occluded from the camera viewpoint.
