Rethinking the imaging pipeline for energy-efficient privacy-preserving continuous mobile vision

Current mobile imaging pipelines, provisioned for high quality photography, are ill-suited for wearable vision analytics, due to their high power consumption and privacy concerns, as exemplified by the slow adoption of wearables, such as Google Glass. Rather than constructing incremental improvements, we believe it is necessary to completely redesign a dedicated imaging pipeline for vision analytics. Toward this goal, we study a novel imaging pipeline, revolving around an in-imager analog vision processor that exports a low bandwidth irreversibly encoded signal, generating vision features before analog-to-digital conversion. To produce this signal at low power, we introduce energy-scaling mechanisms into the imager's analog frontend to produce the encoded signal We use these mechanisms to generate a low-power signal that cannot be used to reconstruct the image, yet suffices as input for vision analytics. This imaging pipeline design will simultaneously achieve privacy and efficiency for continuous mobile vision tasks.