  The digital retina in smart cities is to select what the City Eye tells the
City Brain, and convert the acquired visual data from front-end visual sensors
to features in an intelligent sensing manner. By deploying deep learning and/or
handcrafted models in front-end devices, the compact features can be extracted
and subsequently delivered to back-end cloud for search and advanced analytics.
In this context, we propose a model generation, utilization, and communication
paradigm, aiming to address a set of unique challenges for better artificial
intelligence services in smart cities. In particular, we present an integrated
multiple deep learning models reuse and prediction strategy, which greatly
increases the feasibility of the digital retina in processing and analyzing the
large-scale visual data in smart cities. The promise of the proposed paradigm
is demonstrated through a set of experiments.
