Towards Real Time Egocentric Segment Captioning for The Blind and Visually Impaired in RGB-D Theatre Images
This addresses the specific problem of scene understanding for blind and visually impaired individuals in theatre environments, representing an incremental application of existing methods to a new domain.
The paper tackles the problem of helping blind and visually impaired people understand their surroundings by proposing an approach for egocentric segment captioning that provides descriptions with positions of regions and objects relative to the user, applied to theatre plays using the TS-RGBD dataset.
In recent years, image captioning and segmentation have emerged as crucial tasks in computer vision, with applications ranging from autonomous driving to content analysis. Although multiple solutions have emerged to help blind and visually impaired people move around their environment, few are applications that help them understand and rebuild a scene in their minds through text. Most built models focus on helping users move and avoid obstacles, restricting the number of environments blind and visually impaired people can be in. In this paper, we will propose an approach that helps them understand their surroundings using image captioning. The particularity of our research is that we offer them descriptions with positions of regions and objects regarding them (left, right, front), as well as positional relationships between regions, while we aim to give them access to theatre plays by applying the solution to our TS-RGBD dataset.