Energy-Efficient Parking Analytics System using Deep Reinforcement Learning

TL;DR

This paper introduces RL-CamSleep, a deep reinforcement learning method that intelligently activates parking video analytics cameras to significantly reduce energy consumption while maintaining high accuracy.

Contribution

It presents a novel RL-based approach to selectively activate cameras in parking analytics, reducing energy use by over 76% without sacrificing accuracy.

Findings

Energy consumption reduced by 76.38%.

Achieved over 98% accuracy in parking analytics.

Adaptive policies outperform static activation strategies.

Abstract

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.