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

re-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification