Empowering Visually Impaired Individuals: A Novel Use of Apple Live
  Photos and Android Motion Photos
Seyedalireza Khoshsirat Chandra Kambhamettu
Numerous applications have been developed to assist visually impaired
individuals that employ a machine learning unit to process visual input.
However, a critical challenge with these applications is the sub-optimal
quality of images captured by the users. Given the complexity of operating a
camera for visually impaired individuals, we advocate for the use of Apple Live
Photos and Android Motion Photos technologies. In this study, we introduce a
straightforward methodology to evaluate and contrast the efficacy of
Live/Motion Photos against traditional image-based approaches. Our findings
reveal that both Live Photos and Motion Photos outperform single-frame images
in common visual assisting tasks, specifically in object classification and
VideoQA. We validate our results through extensive experiments on the ORBIT
dataset, which consists of videos collected by visually impaired individuals.
Furthermore, we conduct a series of ablation studies to delve deeper into the
impact of deblurring and longer temporal crops.