Panoptes is a novel view virtualization system for steerable infrastructure cameras. Infrastructure cameras are often installed with a specific application in mind and their view is carefully adjusted to fit this application. Supporting multiple simultaneous analytics applications on the same camera often creates challenges because their view and image requirements tend to differ. Panoptes breaks this one-to-one binding between camera and the application by leveraging the steerable nature of many of these cameras. We also designed a mobility-aware scheduling algorithm that anticipates object mobility and network/steering latency to maximize the number of simultaneous applications served, while minimizing the number of relevant events missed.

LookUp is a novel shoe sensor based approach for sub-meter location classification in noisy outdoor urban environments. Pedestrians are most likely at risk when crossing the street. LookUp estimates pedestrian risk by sensing if a pedestrian is walking safely on the sidewalk or transitioned into the street. Unlike earlier approaches of dead reckoning and step counting, it can measure the inclination of the ground a person is walking on and detect roadway features such as ramps and curbs, that precisely indicate sidewalk-street transitions. These detections can be used to warn distracted pedestrians, or oncoming vehicles of the pedestrian's presence in-street. LookUp was exhaustively evaluated in the high-clutter Manhattan environment.

We explored smartphone-based GPS sensing for pedestrian safety applications. Through extensive tests conducted in rural, suburban and metropolitan environments, we demonstrate that GPS based techniques perform well in rural and suburban scenarios. However, existing positioning technologies are a bottleneck to precise pedestrian positioning in dense urban environments. GPS cannot distinguish pedestrians in the street, and therefore at higher risk, from pedestrians safely walking on the sidewalk.

Diabetic retinopathy is one of the leading causes of blindness worldwide. We propose a low cost postable smartphone-based decision support system for initial screening of diabetic retinopathy using image analysis and machine learning techniques. We attach a smartphone to a direct handheld ophthalmoscope, where the phone captures fundus images as seen through the opthalmoscope. Through this mobile eye-examination system, we envision making the early screening of diabetic retinopathy accessible, especially where dedicated ophthalmology centers are expensive.