Unsupervised Multi-camera Domain Adaptation for Object Detection in Cultural Sites

Domain adaptation approaches can be used to efficiently train object detectors by leveraging labeled synthetic images, inexpensively generated from 3D models, and unlabeled real images, which are cheaper to obtain than labeled ones. Most of the state-of-the-art techniques consider only one source and one target domain for the adaptation task. However, real world scenarios, such as applications in cultural sites, naturally involve many target domains which arise from the use of different cameras at inference time (e.g. different wearable devices and different smartphones on which the algorithm will be deployed). In this work, we investigate whether the availability of multiple unlabeled target domains can improve domain adaptive object detection algorithms. To study the problem, we propose a new dataset comprising images of 16 different objects rendered from a 3D model as well as images collected in the real environment using two different cameras. We experimentally assess that current domain adaptive object detectors can improve their performance by leveraging the multiple targets. As evidence of the usefulness of explicitly considering multiple target domains, we propose a new unsupervised multi-camera domain adaptation approach for object detection which outperforms current methods. Code and dataset are available at https://iplab.dmi.unict.it/OBJ-MDA/.