Can we cover navigational perception needs of the visually impaired by panoptic segmentation?
This addresses the need for improved environmental understanding for visually impaired people, though it is incremental as it applies an existing method (panoptic segmentation) to a new application domain.
The paper tackles the problem of providing holistic navigational perception for visually impaired individuals by proposing the use of panoptic segmentation, which unifies semantic and instance segmentation to offer awareness of both 'things' and 'stuff' in their surroundings, and demonstrates this through a wearable assistive system.
Navigational perception for visually impaired people has been substantially promoted by both classic and deep learning based segmentation methods. In classic visual recognition methods, the segmentation models are mostly object-dependent, which means a specific algorithm has to be devised for the object of interest. In contrast, deep learning based models such as instance segmentation and semantic segmentation allow to individually recognize part of the entire scene, namely things or stuff, for blind individuals. However, both of them can not provide a holistic understanding of the surroundings for the visually impaired. Panoptic segmentation is a newly proposed visual model with the aim of unifying semantic segmentation and instance segmentation. Motivated by that, we propose to utilize panoptic segmentation as an approach to navigating visually impaired people by offering both things and stuff awareness in the proximity of the visually impaired. We demonstrate that panoptic segmentation is able to equip the visually impaired with a holistic real-world scene perception through a wearable assistive system.