  Advances in deep vision techniques and ubiquity of smart cameras will drive
the next generation of video analytics. However, video analytics applications
consume vast amounts of energy as both deep learning techniques and cameras are
power-hungry. In this paper, we focus on a parking video analytics platform and
propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate
the cameras to reduce the energy footprint while retaining the system's
utility. Our key insight is that many video-analytics applications do not
always need to be operational, and we can design policies to activate video
analytics only when necessary. Moreover, our work is complementary to existing
work that focuses on improving hardware and software efficiency. We evaluate
our approach on a city-scale parking dataset having 76 streets spread across
the city. Our analysis demonstrates how streets have various parking patterns,
highlighting the importance of an adaptive policy. Our approach can learn such
an adaptive policy that can reduce the average energy consumption by 76.38% and
achieve an average accuracy of more than 98% in performing video analytics.
