Paper Info
Title
Energy-efficient parking analytics system using deep reinforcement learning
Abstract
ABSTRACTAdvances 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.
Year
DOI
Venue
2021
10.1145/3486611.3486660
Embedded Network Sensor Systems
DocType
ISSN
Citations 
Conference
Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation November 2021 Pages 81-90
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yoones Rezaei100.34
Stephen Lee200.68
Daniel Mossé32184148.86