  Recent works have shown that objects discovery can largely benefit from the
inherent motion information in video data. However, these methods lack a proper
background processing, resulting in an over-segmentation of the non-object
regions into random segments. This is a critical limitation given the
unsupervised setting, where object segments and noise are not distinguishable.
To address this limitation we propose BMOD, a Background-aware Motion-guided
Objects Discovery method. Concretely, we leverage masks of moving objects
extracted from optical flow and design a learning mechanism to extend them to
the true foreground composed of both moving and static objects. The background,
a complementary concept of the learned foreground class, is then isolated in
the object discovery process. This enables a joint learning of the objects
discovery task and the object/non-object separation. The conducted experiments
on synthetic and real-world datasets show that integrating our background
handling with various cutting-edge methods brings each time a considerable
improvement. Specifically, we improve the objects discovery performance with a
large margin, while establishing a strong baseline for object/non-object
separation.
