# Energy-Efficient Parking Analytics System using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2202.08973 · HTML: https://mlsanity.com/paper/2202.08973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning](https://mlsanity.com/paper/1706.09829/) (2017)
- [Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks](https://mlsanity.com/paper/1606.09367/) (2016)
- [ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning](https://mlsanity.com/paper/1909.01968/) (2019)
- [Approximate Query Service on Autonomous IoT Cameras](https://mlsanity.com/paper/1909.00841/) (2019)

## Abstract (arXiv)

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.