  Conventional approaches to object instance re-identification rely on matching
appearances of the target objects among a set of frames. However, learning
appearances of the objects alone might fail when there are multiple objects
with similar appearance or multiple instances of same object class present in
the scene. This paper proposes that partial observations of the background can
be utilized to aid in the object re-identification task for a rigid scene,
especially a rigid environment with a lot of reoccurring identical models of
objects. Using an extension to the Mask R-CNN architecture, we learn to encode
the important and distinct information in the background jointly with the
foreground relevant to rigid real-world scenarios such as an indoor environment
where objects are static and the camera moves around the scene. We demonstrate
the effectiveness of our joint visual feature in the re-identification of
objects in the ScanNet dataset and show a relative improvement of around 28.25%
in the rank-1 accuracy over the deepSort method.
