PreSight: Enabling Real-Time Detection of Accessibility Problems on Sidewalks

Accessibility problems such as obstacles on sidewalks can make navigation dangerous for the visually impaired. Detecting these accessibility problems using embedded cameras is a plausible remedy. However, current computer vision algorithms for object detection rely on exhaustive search with high-dimensional features that present a heavy computational burden and incur a long latency, making them non-ideal for real-time object detection on embedded platforms. To address this problem, inspired by prior-based searching schemes from human vision, we accelerate the machine vision process by using scene-specific features to select candidate regions in the view for further processing. Our system, {\hav} achieves speedup by trading off the workload from on-line detection to off-line prior data collection and extraction. We demonstrate a complete, scalable PreSight prototype to accelerate general computer vision object detection algorithms with focus on detecting of sidewalk accessibility problems. Our prototype system automates the process of creating a geo- tagged database of object-specific priors using crowdsourcing and utilizes this prior knowledge to speedup object detection on embedded platforms. Evaluating under two benchmark object detection algorithms, we demonstrate that the detection latency can be reduced by around 8 times with the aid of PreSight.