Visual information in our surroundings, such as everyday objects and passersby, are often inaccessible to people who are blind. Cameras that leverage egocentric vision, in an attempt to approximate the visual field of the camera wearer, hold great promise for making the visual world more accessible for this population. Typically, such applications rely on pre-trained computer vision models and thus are limited. Moreover, as with any AI systems that augment sensory abilities, conversations around ethical implications and privacy concerns lie at the core of their design and regulation. However, early efforts tend to decouple perspectives, considering only either those of the blind users or potential bystanders.

In this thesis proposal, we revisit egocentric vision for the blind. Through a holistic approach, we examine the following dimensions: type of application (objects and passersby), camera form factor (handheld and wearable), user’s role (a passive consumer and an active director of technology), and privacy concerns (from both end-users and bystanders). Specifically, we propose to design egocentric vision models that understand blind users’ intent and are fine- tuned by the user in the context of object recognition. We seek to explore societal issues that AI-powered cameras may emerge, considering perspectives from both blind users and nearby people whose faces or objects might be captured by the cameras. Last, we propose to investigate interactions and perceptions across different camera form factors to reveal design implications for future work.

Examining Committee: 

 

                          Chair:               Dr. Hernisa Kacorri          
                          Dept rep:         Dr. Michelle Mazurek
                          Members:         Dr. Abhinav Shrivastava
                                                    Dr.  Gregg Vanderheiden